importance of technology research paper

Education Technology: An Evidence-Based Review

In recent years, there has been widespread excitement around the potential for technology to transform learning. As investments in education technology continue to grow, students, parents, and teachers face a seemingly endless array of education technologies from which to choose—from digital personalized learning platforms to educational games to online courses. Amidst the excitement, it is important to step back and understand how technology can help—or in some cases hinder—how students learn. This review paper synthesizes and discusses experimental evidence on the effectiveness of technology-based approaches in education and outlines areas for future inquiry. In particular, we examine RCTs across the following categories of education technology: (1) access to technology, (2) computer-assisted learning, (3) technology-enabled behavioral interventions in education, and (4) online learning. While this review focuses on literature from developed countries, it also draws upon extensive research from developing countries. We hope this literature review will advance the knowledge base of how technology can be used to support education, outline key areas for new experimental research, and help drive improvements to the policies, programs, and structures that contribute to successful teaching and learning.

We are extremely grateful to Caitlin Anzelone, Rekha Balu, Peter Bergman, Brad Bernatek, Ben Castleman, Luke Crowley, Angela Duckworth, Jonathan Guryan, Alex Haslam, Andrew Ho, Ben Jones, Matthew Kraft, Kory Kroft, David Laibson, Susanna Loeb, Andrew Magliozzi, Ignacio Martinez, Susan Mayer, Steve Mintz, Piotr Mitros, Lindsay Page, Amanda Pallais, John Pane, Justin Reich, Jonah Rockoff, Sylvi Rzepka, Kirby Smith, and Oscar Sweeten-Lopez for providing helpful and detailed comments as we put together this review. We also thank Rachel Glennerster for detailed support throughout the project, Jessica Mardo and Sophie Shank for edits, and to the Spencer Foundation for financial support. Any errors or omissions are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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Data science: a game changer for science and innovation

• Regular Paper
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• Published: 19 April 2021
• Volume 11 , pages 263–278, ( 2021 )

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• Valerio Grossi 1 ,
• Fosca Giannotti 1 ,
• Dino Pedreschi 2 ,
• Paolo Manghi 3 ,
• Pasquale Pagano 3 &
• Massimiliano Assante 3  

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This paper shows data science’s potential for disruptive innovation in science, industry, policy, and people’s lives. We present how data science impacts science and society at large in the coming years, including ethical problems in managing human behavior data and considering the quantitative expectations of data science economic impact. We introduce concepts such as open science and e-infrastructure as useful tools for supporting ethical data science and training new generations of data scientists. Finally, this work outlines SoBigData Research Infrastructure as an easy-to-access platform for executing complex data science processes. The services proposed by SoBigData are aimed at using data science to understand the complexity of our contemporary, globally interconnected society.

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What Is Data Science?

Introduction to applied data science, data science.

Avoid common mistakes on your manuscript.

1 Introduction: from data to knowledge

Data science is an interdisciplinary and pervasive paradigm where different theories and models are combined to transform data into knowledge (and value). Experiments and analyses over massive datasets are functional not only to the validation of existing theories and models but also to the data-driven discovery of patterns emerging from data, which can help scientists in the design of better theories and models, yielding a deeper understanding of the complexity of the social, economic, biological, technological, cultural, and natural phenomenon. The products of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. All these aspects are producing a change in the scientific method, in research and in the way our society makes decisions [ 2 ].

Data science emerges to concurring facts: (i) the advent of big data that provides the critical mass of actual examples to learn from, (ii) the advances in data analysis and learning techniques that can produce predictive models and behavioral patterns from big data, and (iii) the advances in high-performance computing infrastructures that make it possible to ingest and manage big data and perform complex analysis [ 16 ].

Paper organization Section 2 discusses how data science impacts our science and society at large in the coming years. Section 3 outlines the main issues related to the ethical problems in studying human behaviors that data science introduces. In Sect.  4 , we show how concepts such as open science and e-infrastructure are effective tools for supporting, disseminating ethical uses of the data, and training new generations of data scientists. We will illustrate the importance of an open data science with examples provided later in the paper. Finally, we show some use cases of data science through thematic environments that bind the datasets with social mining methods.

2 Data science for society, science, industry and business

Data science as an ecosystem: on the left, the figure shows the main components enabling data science (data, analytical methods, and infrastructures). On the right, we can find the impact of data science into society, science, and business. All the activities related to data science should be done under rigid ethical principles

The quality of business decision making, government administration, and scientific research can potentially be improved by analyzing data. Data science offers important insights into many complicated issues, in many instances, with remarkable accuracy and timeliness.

The data science pipeline starts with raw data and transforms them into data used for analytics. The next step is to transform these data into knowledge through analytical methods and then provide results and evaluation measures

As shown in Fig.  1 , data science is an ecosystem where the following scientific, technological, and socioeconomic factors interact:

Data Availability of data and access to data sources;

Analytics & computing infrastructures Availability of high performance analytical processing and open-source analytics;

Skills Availability of highly and rightly skilled data scientists and engineers;

Ethical & legal aspects Availability of regulatory environments for data ownership and usage, data protection and privacy, security, liability, cybercrime, and intellectual property rights;

Applications Business and market ready applications;

Social aspects Focus on major societal global challenges.

Data science envisioned as the intersection between data mining, big data analytics, artificial intelligence, statistical modeling, and complex systems is capable of monitoring data quality and analytical processes results transparently. If we want data science to face the global challenges and become a determinant factor of sustainable development, it is necessary to push towards an open global ecosystem for science, industrial, and societal innovation [ 48 ]. We need to build an ecosystem of socioeconomic activities, where each new idea, product, and service create opportunities for further purposes, and products. An open data strategy, innovation, interoperability, and suitable intellectual property rights can catalyze such an ecosystem and boost economic growth and sustainable development. This strategy also requires a “networked thinking” and a participatory, inclusive approach.

Data are relevant in almost all the scientific disciplines, and a data-dominated science could lead to the solution of problems currently considered hard or impossible to tackle. It is impossible to cover all the scientific sectors where a data-driven revolution is ongoing; here, we shall only provide just a few examples.

The Sloan Digital Sky Survey Footnote 1 has become a central resource for astronomers over the world. Astronomy is being transformed from the one where taking pictures of the sky was a large part of an astronomer’s job, to the one where the images are already in a database, and the astronomer’s task is to find interesting objects and phenomenon in the database. In biological sciences, data are stored in public repositories. There is an entire discipline of bioinformatics that is devoted to the analysis of such data. Footnote 2 Data-centric approaches based on personal behaviors can also support medical applications analyzing data at both human behavior levels and lower molecular ones. For example, integrating genome data of medical reactions with the habits of the users, enabling a computational drug science for high-precision personalized medicine. In humans, as in other organisms, most cellular components exert their functions through interactions with other cellular components. The totality of these interactions (representing the human “interactome”) is a network with hundreds of thousand nodes and a much larger number of links. A disease is rarely a consequence of an abnormality in a single gene. Instead, the disease phenotype is a reflection of various pathological processes that interact in a complex network. Network-based approaches can have multiple biological and clinical applications, especially in revealing the mechanisms behind complex diseases [ 6 ].

Now, we illustrate the typical data science pipeline [ 50 ]. People, machines, systems, factories, organizations, communities, and societies produce data. Data are collected in every aspect of our life, when: we submit a tax declaration; a customer orders an item online; a social media user posts a comment; a X-ray machine is used to take a picture; a traveler sends a review on a restaurant; a sensor in a supply chain sends an alert; or a scientist conducts an experiment. This huge and heterogeneous quantity of data needs to be extracted, loaded, understood, transformed, and in many cases, anonymized before they may be used for analysis. Analysis results include routines, automated decisions, predictions, and recommendations, and outcomes that need to be interpreted to produce actions and feedback. Furthermore, this scenario must also consider ethical problems in managing social data. Figure 2 depicts the data science pipeline. Footnote 3 Ethical aspects are important in the application of data science in several sectors, and they are addressed in Sect.  3 .

2.1 Impact on society

Data science is an opportunity for improving our society and boosting social progress. It can support policymaking; it offers novel ways to produce high-quality and high-precision statistical information and empower citizens with self-awareness tools. Furthermore, it can help to promote ethical uses of big data.

Modern cities are perfect environments densely traversed by large data flows. Using traffic monitoring systems, environmental sensors, GPS individual traces, and social information, we can organize cities as a collective sharing of resources that need to be optimized, continuously monitored, and promptly adjusted when needed. It is easy to understand the potentiality of data science by introducing terms such as urban planning , public transportation , reduction of energy consumption , ecological sustainability, safety , and management of mass events. These terms represent only the front line of topics that can benefit from the awareness that big data might provide to the city stakeholders [ 22 , 27 , 29 ]. Several methods allowing human mobility analysis and prediction are available in the literature: MyWay [ 47 ] exploits individual systematic behaviors to predict future human movements by combining individual and collective learned models. Carpooling [ 22 ] is based on mobility data from travelers in a given territory and constructs a network of potential carpooling users, by exploiting topological properties, highlighting sub-populations with higher chances to create a carpooling community and the propensity of users to be either drivers or passengers in a shared car. Event attendance prediction [ 13 ] analyzes users’ call habits and classifies people into behavioral categories, dividing them among residents, commuters, and visitors and allows to observe the variety of behaviors of city users and the attendance in big events in cities.

Electric mobility is expected to gain importance for the world. The impact of a complete switch to electric mobility is still under investigation, and what appears to be critical is the intensity of flows due to charge (and fast recharge) systems that may challenge the stability of the power network. To avoid instabilities regarding the charging infrastructure, an accurate prediction of power flows associated with mobility is needed. The use of personal mobility data can estimate the mobility flow and simulate the impact of different charging behavioral patterns to predict power flows and optimize the position of the charging infrastructures [ 25 , 49 ]. Lorini et al. [ 26 ] is an example of an urban flood prediction that integrates data provided by CEM system Footnote 4 and Twitter data. Twitter data are processed using massive multilingual approaches for classification. The model is a supervised model which requires a careful data collection and validation of ground truth about confirmed floods from multiple sources.

Another example of data science for society can be found in the development of applications with functions aimed directly at the individual. In this context, concepts such as personal data stores and personal data analytics are aimed at implementing a new deal on personal data, providing a user-centric view where data are collected, integrated and analyzed at the individual level, and providing the user with better awareness of own behavioral, health, and consumer profiles. Within this user-centric perspective, there is room for an even broader market of business applications, such as high-precision real-time targeted marketing, e.g., self-organizing decision making to preserve desired global properties, and sustainability of the transportation or the healthcare system. Such contexts emphasize two essential aspects of data science: the need for creativeness to exploit and combine the several data sources in novel ways and the need to give awareness and control of the personal data to the users that generate them, to sustain a transparent, trust-based, crowd-sourced data ecosystem [ 19 ].

The impact of online social networks in our society has changed the mechanisms behind information spreading and news production. The transformation of media ecosystems and news consumption are having consequences in several fields. A relevant example is the impact of misinformation on society, as for the Brexit referendum when the massive diffusion of fake news has been considered one of the most relevant factors of the outcome of this political event. Examples of achievements are provided by the results regarding the influence of external news media on polarization in online social networks. These achievements indicate that users are highly polarized towards news sources, i.e., they cite (and tend to cite) sources that they identify as ideologically similar to them. Other results regard echo chambers and the role of social media users: there is a strong correlation between the orientation of the content produced and consumed. In other words, an opinion “echoes” back to the user when others are sharing it in the “chamber” (i.e., the social network around the user) [ 36 ]. Other results worth mentioning regard efforts devoted to uncovering spam and bot activities in stock microblogs on Twitter: taking inspiration from biological DNA, the idea is to model the online users’ behavior through strings of characters representing sequences of online users’ actions. As a result of the following papers, [ 11 , 12 ] report that 71% of suspicious users were classified as bots; furthermore, 37% of them also got suspended by Twitter few months after our investigation. Several approaches can be found in the literature. However, they generally display some limitations. Some of them work only on some of the features of the diffusion of misinformation (bot detections, segregation of users due to their opinions or other social analysis), or there is a lack of comprehensive frameworks for interpreting results. While the former case is somehow due to the innovation of the research field and it is explainable, the latter showcases a more fundamental need, as, without strict statistical validation, it is hard to state which are the crucial elements that permit a well-grounded description of a system. For avoiding fake news diffusion, we can state that building a comprehensive fake news dataset providing all information about publishers, shared contents, and the engagements of users over space and time, together with their profile stories, can help the development of innovative and effective learning models. Both unsupervised and supervised methods will work together to identify misleading information. Multidisciplinary teams made up of journalists, linguists, and behavioral scientists and similar will be needed to identify what amounts to information warfare campaigns. Cyberwarfare and information warfare will be two of the biggest threats the world will face in the 21st Century.

Social sensing methods collect data produced by digital citizens, by either opportunistic or participatory crowd-sensing, depending on users’ awareness of their involvement. These approaches present a variety of technological and ethical challenges. An example is represented by Twitter Monitor [ 10 ], that is crowd-sensing tool designed to access Twitter streams through the Twitter Streaming API. It allows launching parallel listening for collecting different sets of data. Twitter Monitor represents a tool for creating services for listening campaigns regarding relevant events such as political elections, natural and human-made disasters, popular national events, etc. [ 11 ]. This campaign can be carried out, specifying keywords, accounts, and geographical areas of interest.

Nowcasting Footnote 5 financial and economic indicators focus on the potential of data science as a proxy for well-being and socioeconomic applications. The development of innovative research methods has demonstrated that poverty indicators can be approximated by social and behavioral mobility metrics extracted from mobile phone data and GPS data [ 34 ]; and the Gross Domestic Product can be accurately nowcasted by using retail supermarket market data [ 18 ]. Furthermore, nowcasting of demographic aspects of territory based on Twitter data [ 1 ] can support official statistics, through the estimation of location, occupation, and semantics. Networks are a convenient way to represent the complex interaction among the elements of a large system. In economics, networks are gaining increasing attention because the underlying topology of a networked system affects the aggregate output, the propagation of shocks, or financial distress; or the topology allows us to learn something about a node by looking at the properties of its neighbors. Among the most investigated financial and economic networks, we cite a work that analyzes the interbank systems, the payment networks between firms, the banks-firms bipartite networks, and the trading network between investors [ 37 ]. Another interesting phenomenon is the advent of blockchain technology that has led to the innovation of bitcoin crypto-currency [ 31 ].

Data science is an excellent opportunity for policy, data journalism, and marketing. The online media arena is now available as a real-time experimenting society for understanding social mechanisms, like harassment, discrimination, hate, and fake news. In our vision, the use of data science approaches is necessary for better governance. These new approaches integrate and change the Official Statistics representing a cheaper and more timely manner of computing them. The impact of data science-driven applications can be particularly significant when the applications help to build new infrastructures or new services for the population.

The availability of massive data portraying soccer performance has facilitated recent advances in soccer analytics. Rossi et al. [ 42 ] proposed an innovative machine learning approach to the forecasting of non-contact injuries for professional soccer players. In [ 3 ], we can find the definition of quantitative measures of pressing in defensive phases in soccer. Pappalardo et al. [ 33 ] outlined the automatic and data-driven evaluation of performance in soccer, a ranking system for soccer teams. Sports data science is attracting much interest and is now leading to the release of a large and public dataset of sports events.

Finally, data science has unveiled a shift from population statistics to interlinked entities statistics, connected by mutual interactions. This change of perspective reveals universal patterns underlying complex social, economic, technological, and biological systems. It is helpful to understand the dynamics of how opinions, epidemics, or innovations spread in our society, as well as the mechanisms behind complex systemic diseases, such as cancer and metabolic disorders revealing hidden relationships between them. Considering diffusive models and dynamic networks, NDlib [ 40 ] is a Python package for the description, simulation, and observation of diffusion processes in complex networks. It collects diffusive models from epidemics and opinion dynamics and allows a scientist to compare simulation over synthetic systems. For community discovery, two tools are available for studying the structure of a community and understand its habits: Demon [ 9 ] extracts ego networks (i.e., the set of nodes connected to an ego node) and identifies the real communities by adopting a democratic, bottom-up merging approach of such structures. Tiles [ 41 ] is dedicated to dynamic network data and extracts overlapping communities and tracks their evolution in time following an online iterative procedure.

2.2 Impact on industry and business

Data science can create an ecosystem of novel data-driven business opportunities. As a general trend across all sectors, massive quantities of data will be made accessible to everybody, allowing entrepreneurs to recognize and to rank shortcomings in business processes, to spot potential threads and win-win situations. Ideally, every citizen could establish from these patterns new business ideas. Co-creation enables data scientists to design innovative products and services. The value of joining different datasets is much larger than the sum of the value of the separated datasets by sharing data of various nature and provenance.

The gains from data science are expected across all sectors, from industry and production to services and retail. In this context, we cite several macro-areas where data science applications are especially promising. In energy and environment , the digitization of the energy systems (from production to distribution) enables the acquisition of real-time, high-resolution data. Coupled with other data sources, such as weather data, usage patterns, and market data (accompanied by advanced analytics), efficiency levels can be increased immensely. The positive impact to the environment is also enhanced by geospatial data that help to understand how our planet and its climate are changing and to confront major issues such as global warming, preservation of the species, the role and effects of human activities.

The manufacturing and production sector with the growing investments into Industry 4.0 and smart factories with sensor-equipped machinery that are both intelligent and networked (see internet of things . Cyber-physical systems ) will be one of the major producers of data in the world. The application of data science into this sector will bring efficiency gains and predictive maintenance. Entirely new business models are expected since the mass production of individualized products becomes possible where consumers may have direct access to influence and control.

As already stated in Sect.  2.1 , data science will contribute to increasing efficiency in public administrations processes and healthcare. In the physical and the cyber-domain, security will be enhanced. From financial fraud to public security, data science will contribute to establishing a framework that enables a safe and secure digital economy. Big data exploitation will open up opportunities for innovative, self-organizing ways of managing logistical business processes. Deliveries could be based on predictive monitoring, using data from stores, semantic product memories, internet forums, and weather forecasts, leading to both economic and environmental savings. Let us also consider the impact of personalized services for creating real experiences for tourists. The analysis of real-time and context-aware data (with the help of historical and cultural heritage data) will provide customized information to each tourist, and it will contribute to the better and more efficient management of the whole tourism value chain.

3 Data science ethics

Data science creates great opportunities but also new risks. The use of advanced tools for data analysis could expose sensitive knowledge of individual persons and could invade their privacy. Data science approaches require access to digital records of personal activities that contain potentially sensitive information. Personal information can be used to discriminate people based on their presumed characteristics. Data-driven algorithms yield classification and prediction models of behavioral traits of individuals, such as credit score, insurance risk, health status, personal preferences, and religious, ethnic, or political orientation, based on personal data disseminated in the digital environment by users (with or often without their awareness). The achievements of data science are the result of re-interpreting available data for analysis goals that differ from the original reasons motivating data collection. For example, mobile phone call records are initially collected by telecom operators for billing and operational aims, but they can be used for accurate and timely demography and human mobility analysis at a country or regional scale. This re-purposing of data clearly shows the importance of legal compliance and data ethics technologies and safeguards to protect privacy and anonymity; to secure data; to engage users; to avoid discrimination and misuse; to account for transparency; and to the purpose of seizing the opportunities of data science while controlling the associated risks.

Several aspects should be considered to avoid to harm individual privacy. Ethical elements should include the: (i) monitoring of the compliance of experiments, research protocols, and applications with ethical and juridical standards; (ii) developing of big data analytics and social mining tools with value-sensitive design and privacy-by-design methodologies; (iii) boosting of excellence and international competitiveness of Europe’s big data research in safe and fair use of big data for research. It is essential to highlight that data scientists using personal and social data also through infrastructures have the responsibility to get acquainted with the fundamental ethical aspects relating to becoming a “data controller.” This aspect has to be considered to define courses for informing and training data scientists about the responsibilities, the possibilities, and the boundaries they have in data manipulation.

Recalling Fig.  2 , it is crucial to inject into the data science pipeline the ethical values of fairness : how to avoid unfair and discriminatory decisions; accuracy : how to provide reliable information; confidentiality : how to protect the privacy of the involved people and transparency : how to make models and decisions comprehensible to all stakeholders. This value-sensitive design has to be aimed at boosting widespread social acceptance of data science, without inhibiting its power. Finally, it is essential to consider also the impact of the General Data Protection Regulation (GDPR) on (i) companies’ duties and how these European companies should comply with the limits in data manipulation the Regulation requires; and on (ii) researchers’ duties and to highlight articles and recitals which specifically mention and explain how research is intended in GDPR’s legal system.

The relationship between big and open data and how they relate to the broad concept of open government

We complete this section with another important aspect related to open data, i.e., accessible public data that people, companies, and organizations can use to launch new ventures, analyze patterns and trends, make data-driven decisions, and solve complex problems. All the definitions of open data include two features: (i) the data must be publicly available for anyone to use, and (ii) data must be licensed in a way that allows for its reuse. All over the world, initiatives are launched to make data open by government agencies and public organizations; listing them is impossible, but an UN initiative has to be mentioned. Global Pulse Footnote 6 meant to implement the vision for a future in which big data is harnessed safely and responsibly as a public good.

Figure 3 shows the relationships between open data and big data. Currently, the problem is not only that government agencies (and some business companies) are collecting personal data about us, but also that we do not know what data are being collected and we do not have access to the information about ourselves. As reported by the World Economic forum in 2013, it is crucial to understand the value of personal data to let the users make informed decisions. A new branch of philosophy and ethics is emerging to handle personal data related issues. On the one hand, in all cases where the data might be used for the social good (i.e., medical research, improvement of public transports, contrasting epidemics), and understanding the personal data value means to correctly evaluate the balance between public benefits and personal loss of protection. On the other hand, when data are aimed to be used for commercial purposes, the value mentioned above might instead translate into simple pricing of personal information that the user might sell to a company for its business. In this context, discrimination discovery consists of searching for a-priori unknown contexts of suspect discrimination against protected-by-law social groups, by analyzing datasets of historical decision records. Machine learning and data mining approaches may be affected by discrimination rules, and these rules may be deeply hidden within obscure artificial intelligence models. Thus, discrimination discovery consists of understanding whether a predictive model makes direct or indirect discrimination. DCube [ 43 ] is a tool for data-driven discrimination discovery, a library of methods on fairness analysis.

It is important to evaluate how a mining model or algorithm takes its decision. The growing field of methods for explainable machine learning provides and continuously expands a set of comprehensive tool-kits [ 21 ]. For example, X-Lib is a library containing state-of-the-art explanation methods organized within a hierarchical structure and wrapped in a similar fashion way such that they can be easily accessed and used from different users. The library provides support for explaining classification on tabular data and images and for explaining the logic of complex decision systems. X-Lib collects, among the others, the following collection of explanation methods: LIME [ 38 ], Anchor [ 39 ], DeepExplain that includes Saliency maps [ 44 ], Gradient * Input, Integrated Gradients, and DeepLIFT [ 46 ]. Saliency method is a library containing code for SmoothGrad [ 45 ], as well as implementations of several other saliency techniques: Vanilla Gradients, Guided Backpropogation, and Grad-CAM. Another improvement in this context is the use of robotics and AI in data preparation, curation, and in detecting bias in data, information and knowledge as well as in the misuse and abuse of these assets when it comes to legal, privacy, and ethical issues and when it comes to transparency and trust. We cannot rely on human beings to do these tasks. We need to exploit the power of robotics and AI to help provide the protections required. Data and information lawyers will play a key role in legal and privacy issues, ethical use of these assets, and the problem of bias in both algorithms and the data, information, and knowledge used to develop analytics solutions. Finally, we can state that data science can help to fill the gap between legislators and technology.

4 Big data ecosystem: the role of research infrastructures

Research infrastructures (RIs) play a crucial role in the advent and development of data science. A social mining experiment exploits the main components of data science depicted in Fig.  1 (i.e., data, infrastructures, analytical methods) to enable multidisciplinary scientists and innovators to extract knowledge and to make the experiment reusable by the scientific community, innovators providing an impact on science and society.

Resources such as data and methods help domain and data scientists to transform research or an innovation question into a responsible data-driven analytical process. This process is executed onto the platform, thus supporting experiments that yield scientific output, policy recommendations, or innovative proofs-of-concept. Furthermore, an operational ethical board’s stewardship is a critical factor in the success of a RI.

An infrastructure typically offers easy-to-use means to define complex analytical processes and workflows , thus bridging the gap between domain experts and analytical technology. In many instances, domain experts may become a reference for their scientific communities, thus facilitating new users engagement within the RI activities. As a collateral feedback effect, experiments will generate new relevant data, methods, and workflows that can be integrated into the platform by data scientists, contributing to the resource expansion of the RI. An experiment designed in a node of the RI and executed on the platform returns its results to the entire RI community.

Well defined thematic environments amplify new experiments achievements towards the vertical scientific communities (and potential stakeholders) by activating appropriate dissemination channels.

4.1 The SoBigData Research Infrastructure

The SoBigData Research Infrastructure Footnote 7 is an ecosystem of human and digital resources, comprising data scientists, analytics, and processes. As shown in Fig.  4 , SoBigData is designed to enable multidisciplinary scientists and innovators to realize social mining experiments and to make them reusable by the scientific communities. All the components have been introduced for implementing data science from raw data management to knowledge extraction, with particular attention to legal and ethical aspects as reported in Fig.  1 . SoBigData supports data science serving a cross-disciplinary community of data scientists studying all the elements of societal complexity from a data- and model-driven perspective.

Currently, SoBigData includes scientific, industrial, and other stakeholders. In particular, our stakeholders are data analysts and researchers (35.6%), followed by companies (33.3%) and policy and lawmakers (20%). The following sections provide a short but comprehensive overview of the services provided by SoBigData RI with special attention on supporting ethical and open data science [ 15 , 16 ].

4.1.1 Resources, facilities, and access opportunities

Over the past decade, Europe has developed world-leading expertise in building and operating e-infrastructures. They are large-scale, federated and distributed online research environments through which researchers can share access to scientific resources (including data, instruments, computing, and communications), regardless of their location. They are meant to support unprecedented scales of international collaboration in science, both within and across disciplines, investing in economy-of-scale and common behavior, policies, best practices, and standards. They shape up a common environment where scientists can create , validate , assess , compare , and share their digital results of science, such as research data and research methods, by using a common “digital laboratory” consisting of agreed-on services and tools.

The SoBigData Research Infrastructure: an ecosystem of human and digital resources, comprising data scientists, analytical methods, and processes. SoBigData enables multidisciplinary scientists and innovators to carry out experiments and to make them reusable by the community

However, the implementation of workflows, possibly following Open Science principles of reproducibility and transparency, is hindered by a multitude of real-world problems. One of the most prominent is that e-infrastructures available to research communities today are far from being well-designed and consistent digital laboratories, neatly designed to share and reuse resources according to common policies, data models, standards, language platforms, and APIs. They are instead “patchworks of systems,” assembling online tools, services, and data sources and evolving to match the requirements of the scientific process, to include new solutions. The degree of heterogeneity excludes the adoption of uniform workflow management systems, standard service-oriented approaches, routine monitoring and accounting methods. The realization of scientific workflows is typically realized by writing ad hoc code, manipulating data on desktops, alternating the execution of online web services, sharing software libraries implementing research methods in different languages, desktop tools, web-accessible execution engines (e.g., Taverna, Knime, Galaxy).

The SoBigData e-infrastructure is based on D4Science services, which provides researchers and practitioners with a working environment where open science practices are transparently promoted, and data science practices can be implemented by minimizing the technological integration cost highlighted above.

D4Science is a deployed instance of the gCube Footnote 8 technology [ 4 ], a software conceived to facilitate the integration of web services, code, and applications as resources of different types in a common framework, which in turn enables the construction of Virtual Research Environments (VREs) [ 7 ] as combinations of such resources (Fig.  5 ). As there is no common framework that can be trusted enough, sustained enough, to convince resource providers that converging to it would be a worthwhile effort, D4Science implements a “system of systems.” In such a framework, resources are integrated with minimal cost, to gain in scalability, performance, accounting, provenance tracking, seamless integration with other resources, visibility to all scientists. The principle is that the cost of “participation” to the framework is on the infrastructure rather than on resource providers. The infrastructure provides the necessary bridges to include and combine resources that would otherwise be incompatible.

D4Science: resources from external systems, virtual research environments, and communities

More specifically, via D4Science, SoBigData scientists can integrate and share resources such as datasets, research methods, web services via APIs, and web applications via Portlets. Resources can then be integrated, combined, and accessed via VREs, intended as web-based working environments tailored to support the needs of their designated communities, each working on a research question. Research methods are integrated as executable code, implementing WPS APIs in different programming languages (e.g., Java, Python, R, Knime, Galaxy), which can be executed via the Data Miner analytics platform in parallel, transparently to the users, over powerful and extensible clusters, and via simple VRE user interfaces. Scientists using Data Miner in the context of a VRE can select and execute the available methods and share the results with other scientists, who can repeat or reproduce the experiment with a simple click.

D4Science VREs are equipped with core services supporting data analysis and collaboration among its users: ( i ) a shared workspace to store and organize any version of a research artifact; ( ii ) a social networking area to have discussions on any topic (including working version and released artifacts) and be informed on happenings; ( iii ) a Data Miner analytics platform to execute processing tasks (research methods) either natively provided by VRE users or borrowed from other VREs to be applied to VRE users’ cases and datasets; and iv ) a catalogue-based publishing platform to make the existence of a certain artifact public and disseminated. Scientists operating within VREs use such facilities continuously and transparently track the record of their research activities (actions, authorship, provenance), as well as products and links between them (lineage) resulting from every phase of the research life cycle, thus facilitating publishing of science according to Open Science principles of transparency and reproducibility [ 5 ].

Today, SoBigData integrates the resources in Table  1 . By means of such resources, SoBigData scientists have created VREs to deliver the so-called SoBigData exploratories : Explainable Machine Learning , Sports Data Science , Migration Studies , Societal Debates , Well-being & Economy , and City of Citizens . Each exploratory includes the resources required to perform Data science workflows in a controlled and shared environment. Resources range from data to methods, described more in detail in the following, together with their exploitation within the exploratories.

All the resources and instruments integrate into SoBigData RI are structured in such a way as to operate within the confines of the current data protection law with the focus on General Data Protection Regulation (GDPR) and ethical analysis of the fundamental values involved in social mining and AI. Each item into the catalogue has specific fields for managing ethical issues (e.g., if a dataset contains personal info) and fields for describing and managing intellectual properties.

4.1.2 Data resources: social mining and big data ecosystem

SoBigData RI defines policies supporting users in the collection, description, preservation, and sharing of their data sets. It implements data science making such data available for collaborative research by adopting various strategies, ranging from sharing the open data sets with the scientific community at large, to share the data with disclosure restriction allowing data access within secure environments.

Several big data sets are available through SoBigData RI including network graphs from mobile phone call data; networks crawled from many online social networks, including Facebook and Flickr, transaction micro-data from diverse retailers, query logs both from search engines and e-commerce, society-wide mobile phone call data records, GPS tracks from personal navigation devices, survey data about customer satisfaction or market research, extensive web archives, billions of tweets, and data from location-aware social networks.

4.1.3 Data science through SoBigData exploratories

Exploratories are thematic environments built on top of the SoBigData RI. An exploratory binds datasets with social mining methods providing the research context for supporting specific data science applications by: (i) providing the scientific context for performing the application. This context can be considered a container for binding specific methods, applications, services, and datasets; (ii) stimulating communities on the effectiveness of the analytical process related to the analysis, promoting scientific dissemination, result sharing, and reproducibility. The use of exploratories promotes the effectiveness of the data science trough research infrastructure services. The following sections report a short description of the six SoBigData exploratories. Figure 6 shows the main thematic areas covered by each exploratory. Due to its nature, Explainable Machine Learning exploratory can be applied to each sector where a black-box machine learning approach is used. The list of exploratories (and the data and methods inside them) are updated continuously and continue to grow over time. Footnote 9

SoBigData covers six thematic areas listed horizontally. Each exploratory covers more than one thematic area

City of citizens. This exploratory aims to collect data science applications and methods related to geo-referenced data. The latter describes the movements of citizens in a city, a territory, or an entire region. There are several studies and different methods that employ a wide variety of data sources to build models about the mobility of people and city characteristics in the scientific literature [ 30 , 32 ]. Like ecosystems, cities are open systems that live and develop utilizing flows of energy, matter, and information. What distinguishes a city from a colony is the human component (i.e., the process of transformation by cultural and technological evolution). Through this combination, cities are evolutionary systems that develop and co-evolve continuously with their inhabitants [ 24 ]. Cities are kaleidoscopes of information generated by a myriad of digital devices weaved into the urban fabric. The inclusion of tracking technologies in personal devices enabled the analysis of large sets of mobility data like GPS traces and call detail records.

Data science applied to human mobility is one of the critical topics investigated in SoBigData thanks to the decennial experience of partners in European projects. The study of human mobility led to the integration into the SoBigData of unique Global Positioning System (GPS) and call detail record (CDR) datasets of people and vehicle movements, and geo-referenced social network data as well as several mobility services: O/D (origin-destination) matrix computation, Urban Mobility Atlas Footnote 10 (a visual interface to city mobility patterns), GeoTopics Footnote 11 (for exploring patterns of urban activity from Foursquare), and predictive models: MyWay Footnote 12 (trajectory prediction), TripBuilder Footnote 13 (tourists to build personalized tours of a city). In human mobility, research questions come from geographers, urbanists, complexity scientists, data scientists, policymakers, and Big Data providers, as well as innovators aiming to provide applications for any service for the smart city ecosystem. The idea is to investigate the impact of political events on the well-being of citizens. This exploratory supports the development of “happiness” and “peace” indicators through text mining/opinion mining pipeline on repositories of online news. These indicators reveal that the level of crime of a territory can be well approximated by analyzing the news related to that territory. Generally, we study the impact of the economy on well-being and vice versa, e.g., also considering the propagation of shocks of financial distress in an economic or financial system crucially depends on the topology of the network interconnecting the different elements.

Well-being and economy. This exploratory tests the hypothesis that well-being is correlated to the business performance of companies. The idea is to combine statistical methods and traditional economic data (typically at low-frequency) with high-frequency data from non-traditional sources, such as, i.e., web, supermarkets, for now-casting economic, socioeconomic and well-being indicators. These indicators allow us to study and measure real-life costs by studying price variation and socioeconomic status inference. Furthermore, this activity supports studies on the correlation between people’s well-being and their social and mobility data. In this context, some basic hypothesis can be summarized as: (i) there are curves of age- and gender-based segregation distribution in boards of companies, which are characteristic to mean credit risk of companies in a region; (ii) low mean credit risk of companies in a region has a positive correlation to well-being; (iii) systemic risk correlates highly with well-being indices at a national level. The final aim is to provide a set of guidelines to national governments, methods, and indices for decision making on regulations affecting companies to improve well-being in the country, also considering effective policies to reduce operational risks such as credit risk, and external threats of companies [ 17 ].

Big Data, analyzed through the lenses of data science, provides means to understand our complex socioeconomic and financial systems. On the one hand, this offers new opportunities to measure the patterns of well-being and poverty at a local and global scale, empowering governments and policymakers with the unprecedented opportunity to nowcast relevant economic quantities and compare different countries, regions, and cities. On the other hand, this allows us to investigate the network underlying the complex systems of economy and finance, and it affects the aggregate output, the propagation of shocks or financial distress and systemic risk.

Societal debates. This exploratory employs data science approaches to answer research questions such as who is participating in public debates? What is the “big picture” response from citizens to a policy, election, referendum, or other political events? This kind of analysis allows scientists, policymakers, and citizens to understand the online discussion surrounding polarized debates [ 14 ]. The personal perception of online discussions on social media is often biased by the so-called filter bubble, in which automatic curation of content and relationships between users negatively affects the diversity of opinions available to them. Making a complete analysis of online polarized debates enables the citizens to be better informed and prepared for political outcomes. By analyzing content and conversations on social media and newspaper articles, data scientists study public debates and also assess public sentiment around debated topics, opinion diffusion dynamics, echo chambers formation and polarized discussions, fake news analysis, and propaganda bots. Misinformation is often the result of a distorted perception of concepts that, although unrelated, suddenly appear together in the same narrative. Understanding the details of this process at an early stage may help to prevent the birth and the diffusion of fake news. The misinformation fight includes the development of dynamical models of misinformation diffusion (possibly in contrast to the spread of mainstream news) as well as models of how attention cycles are accelerated and amplified by the infrastructures of online media.

Another important topic covered by this exploratory concerns the analysis of how social bots activity affects fake news diffusion. Determining whether a human or a bot controls a user account is a complex task. To the best of our knowledge, the only openly accessible solution to detect social bots is Botometer, an API that allows us to interact with an underlying machine learning system. Although Botometer has been proven to be entirely accurate in detecting social bots, it has limitations due to the Twitter API features: hence, an algorithm overcoming the barriers of current recipes is needed.

The resources related to Societal Debates exploratory, especially in the domain of media ecology and the fight against misinformation online, provide easy-to-use services to public bodies, media outlets, and social/political scientists. Furthermore, SoBigData supports new simulation models and experimental processes to validate in vivo the algorithms for fighting misinformation, curbing the pathological acceleration and amplification of online attention cycles, breaking the bubbles, and explore alternative media and information ecosystems.

Migration studies. Data science is also useful to understand the migration phenomenon. Knowledge about the number of immigrants living in a particular region is crucial to devise policies that maximize the benefits for both locals and immigrants. These numbers can vary rapidly in space and time, especially in periods of crisis such as wars or natural disasters.

This exploratory provides a set of data and tools for trying to answer some questions about migration flows. Through this exploratory, a data scientist studies economic models of migration and can observe how migrants choose their destination countries. A scientist can discover what is the meaning of “opportunities” that a country provides to migrants, and whether there are correlations between the number of incoming migrants and opportunities in the host countries [ 8 ]. Furthermore, this exploratory tries to understand how public perception of migration is changing using an opinion mining analysis. For example, social network analysis enables us to analyze the migrant’s social network and discover the structure of the social network for people who decided to start a new life in a different country [ 28 ].

Finally, we can also evaluate current integration indices based on official statistics and survey data, which can be complemented by Big Data sources. This exploratory aims to build combined integration indexes that take into account multiple data sources to evaluate integration on various levels. Such integration includes mobile phone data to understand patterns of communication between immigrants and natives; social network data to assess sentiment towards immigrants and immigration; professional network data (such as LinkedIn) to understand labor market integration, and local data to understand to what extent moving across borders is associated with a change in the cultural norms of the migrants. These indexes are fundamental to evaluate the overall social and economic effects of immigration. The new integration indexes can be applied with various space and time resolutions (small area methods) to obtain a complete image of integration, and complement official index.

Sports data science. The proliferation of new sensing technologies that provide high-fidelity data streams extracted from every game, is changing the way scientists, fans and practitioners conceive sports performance. The combination of these (big) data with the tools of data science provides the possibility to unveil complex models underlying sports performance and enables to perform many challenging tasks: from automatic tactical analysis to data-driven performance ranking; game outcome prediction, and injury forecasting. The idea is to foster research on sports data science in several directions. The application of explainable AI and deep learning techniques can be hugely beneficial to sports data science. For example, by using adversarial learning, we can modify the training plans of players that are associated with high injury risk and develop training plans that maximize the fitness of players (minimizing their injury risk). The use of gaming, simulation, and modeling is another set of tools that can be used by coaching staff to test tactics that can be employed against a competitor. Furthermore, by using deep learning on time series, we can forecast the evolution of the performance of players and search for young talents.

This exploratory examines the factors influencing sports success and how to build simulation tools for boosting both individual and collective performance. Furthermore, this exploratory describes performances employing data, statistics, and models, allowing coaches, fans, and practitioners to understand (and boost) sports performance [ 42 ].

Explainable machine learning. Artificial Intelligence, increasingly based on Big Data analytics, is a disruptive technology of our times. This exploratory provides a forum for studying effects of AI on the future society. In this context, SoBigData studies the future of labor and the workforce, also through data- and model-driven analysis, simulations, and the development of methods that construct human understandable explanations of AI black-box models [ 20 ].

Black box systems for automated decision making map a user’s features into a class that predicts the behavioral traits of individuals, such as credit risk, health status, without exposing the reasons why. Most of the time, the internal reasoning of these algorithms is obscure even to their developers. For this reason, the last decade has witnessed the rise of a black box society. This exploratory is developing a set of techniques and tools which allow data analysts to understand why an algorithm produce a decision. These approaches are designed not for discovering a lack of transparency but also for discovering possible biases inherited by the algorithms from human prejudices and artefacts hidden in the training data (which may lead to unfair or wrong decisions) [ 35 ].

5 Conclusions: individual and collective intelligence

The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s [ 23 ]. Since 2012, every day 2.5 exabytes (2.5 \(\times \) 10 \(^18\) bytes) of data were created; as of 2014, every day 2.3 zettabytes (2.3 \(\times \) 10 \(^21\) bytes) of data were generated by Super-power high-tech Corporation worldwide. Soon zettabytes of useful public and private data will be widely and openly available. In the next years, smart applications such as smart grids, smart logistics, smart factories, and smart cities will be widely deployed across the continent and beyond. Ubiquitous broadband access, mobile technology, social media, services, and internet of think on billions of devices will have contributed to the explosion of generated data to a total global estimate of 40 zettabytes.

In this work, we have introduced data science as a new challenge and opportunity for the next years. In this context, we have tried to summarize in a concise way several aspects related to data science applications and their impacts on society, considering both the new services available and the new job perspectives. We have also introduced issues in managing data representing human behavior and showed how difficult it is to preserve personal information and privacy. With the introduction of SoBigData RI and exploratories, we have provided virtual environments where it is possible to understand the potentiality of data science in different research contexts.

Concluding, we can state that social dilemmas occur when there is a conflict between the individual and public interest. Such problems also appear in the ecosystem of distributed AI systems (based on data science tools) and humans, with additional difficulties due: on the one hand, to the relative rigidity of the trained AI systems and the necessity of achieving social benefit, and, on the other hand, to the necessity of keeping individuals interested. What are the principles and solutions for individual versus social optimization using AI, and how can an optimum balance be achieved? The answer is still open, but these complex systems have to work on fulfilling collective goals, and requirements, with the challenge that human needs change over time and move from one context to another. Every AI system should operate within an ethical and social framework in understandable, verifiable, and justifiable way. Such systems must, in any case, work within the bounds of the rule of law, incorporating protection of fundamental rights into the AI infrastructure. In other words, the challenge is to develop mechanisms that will result in the system converging to an equilibrium that complies with European values and social objectives (e.g., social inclusion) but without unnecessary losses of efficiency.

Interestingly, data science can play a vital role in enhancing desirable behaviors in the system, e.g., by supporting coordination and cooperation that is, more often than not, crucial to achieving any meaningful improvements. Our ultimate goal is to build the blueprint of a sociotechnical system in which AI not only cooperates with humans but, if necessary, helps them to learn how to collaborate, as well as other desirable behaviors. In this context, it is also essential to understand how to achieve robustness of the human and AI ecosystems in respect of various types of malicious behaviors, such as abuse of power and exploitation of AI technical weaknesses.

We conclude by paraphrasing Stephen Hawking in his Brief Answers to the Big Questions: the availability of data on its own will not take humanity to the future, but its intelligent and creative use will.

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Grossi, V., Giannotti, F., Pedreschi, D. et al. Data science: a game changer for science and innovation. Int J Data Sci Anal 11 , 263–278 (2021). https://doi.org/10.1007/s41060-020-00240-2

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The societal impacts of technological change can be seen in many domains, from messenger RNA vaccines and automation to drones and climate change. The pace of that technological change can affect its impact, and how quickly a technology improves in performance can be an indicator of its future importance. For decision-makers like investors, entrepreneurs, and policymakers, predicting which technologies are fast improving (and which are overhyped) can mean the difference between success and failure.

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A technology domain, as the researchers define it, consists of sets of artifacts fulfilling a specific function using a specific branch of scientific knowledge. To find the patents that best represent a domain, the team built on previous research conducted by co-author Chris Magee, a professor of the practice of engineering systems within the Institute for Data, Systems, and Society (IDSS). Magee and his colleagues found that by looking for patent overlap between the U.S. and international patent-classification systems, they could quickly identify patents that best represent a technology. The researchers ultimately created a correspondence of all patents within the U.S. patent system to a set of 1,757 technology domains.

To estimate performance improvement, Singh employed a method refined by co-authors Magee and Giorgio Triulzi, a researcher with the Sociotechnical Systems Research Center (SSRC) within IDSS and an assistant professor at Universidad de los Andes in Colombia. Their method is based on the average “centrality” of patents in the patent citation network. Centrality refers to multiple criteria for determining the ranking or importance of nodes within a network.

“Our method provides predictions of performance improvement rates for nearly all definable technologies for the first time,” says Singh.

Those rates vary — from a low of 2 percent per year for the “Mechanical skin treatment — Hair removal and wrinkles” domain to a high of 216 percent per year for the “Dynamic information exchange and support systems integrating multiple channels” domain. The researchers found that most technologies improve slowly; more than 80 percent of technologies improve at less than 25 percent per year. Notably, the number of patents in a technological area was not a strong indicator of a higher improvement rate.

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A new study from the Cornell SC Johnson College of Business advances understanding of the U.S. production chain evolution amidst technological progress in information technology (IT), shedding light on the complex connections between business IT investments and organizational design. Advances in IT have sparked significant changes in how companies design their production processes. In the paper " Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing ," which published April 30 in Management Science, Chris Forman , the Peter and Stephanie Nolan Professor in the Dyson School of Applied Economics and Management , and his co-author delved into what these changes mean for businesses and consumers.

Forman and Kristina McElheran, assistant professor of strategic management at University of Toronto, analyzed U.S. Census Bureau data of over 5,600 manufacturing plants to see how the production chains of businesses were affected by the internet revolution. Their use of census data allowed them to look inside the relationships among production units within and between companies and how transaction flows changed after companies invested in internet-enabled technology that facilitated coordination between them. The production units of many of the companies in their study concurrently sold to internal and external customers, a mix they refer to as plural selling. They found that the reduction in communication costs enabled by the internet shifted the mix toward more sales outside of the firm, or less vertical integration.

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Impacts of digital technologies on education and factors influencing schools' digital capacity and transformation: A literature review

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1 CYENS Center of Excellence & Cyprus University of Technology (Cyprus Interaction Lab), Cyprus, CYENS Center of Excellence & Cyprus University of Technology, Nicosia-Limassol, Cyprus

Ourania Miliou

Yiannis dimitriadis.

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Nikoleta giannoutsou, romina cachia.

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Alejandra Martínez Monés

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Digital technologies have brought changes to the nature and scope of education and led education systems worldwide to adopt strategies and policies for ICT integration. The latter brought about issues regarding the quality of teaching and learning with ICTs, especially concerning the understanding, adaptation, and design of the education systems in accordance with current technological trends. These issues were emphasized during the recent COVID-19 pandemic that accelerated the use of digital technologies in education, generating questions regarding digitalization in schools. Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses. Such results have engendered the need for schools to learn and build upon the experience to enhance their digital capacity and preparedness, increase their digitalization levels, and achieve a successful digital transformation. Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem, there is a need to show how these impacts are interconnected and identify the factors that can encourage an effective and efficient change in the school environments. For this purpose, we conducted a non-systematic literature review. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors that affect the schools’ digital capacity and digital transformation. The findings suggest that ICT integration in schools impacts more than just students’ performance; it affects several other school-related aspects and stakeholders, too. Furthermore, various factors affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the digital transformation process. The study results shed light on how ICTs can positively contribute to the digital transformation of schools and which factors should be considered for schools to achieve effective and efficient change.

Introduction

Digital technologies have brought changes to the nature and scope of education. Versatile and disruptive technological innovations, such as smart devices, the Internet of Things (IoT), artificial intelligence (AI), augmented reality (AR) and virtual reality (VR), blockchain, and software applications have opened up new opportunities for advancing teaching and learning (Gaol & Prasolova-Førland, 2021 ; OECD, 2021 ). Hence, in recent years, education systems worldwide have increased their investment in the integration of information and communication technology (ICT) (Fernández-Gutiérrez et al., 2020 ; Lawrence & Tar, 2018 ) and prioritized their educational agendas to adapt strategies or policies around ICT integration (European Commission, 2019 ). The latter brought about issues regarding the quality of teaching and learning with ICTs (Bates, 2015 ), especially concerning the understanding, adaptation, and design of education systems in accordance with current technological trends (Balyer & Öz, 2018 ). Studies have shown that despite the investment made in the integration of technology in schools, the results have not been promising, and the intended outcomes have not yet been achieved (Delgado et al., 2015 ; Lawrence & Tar, 2018 ). These issues were exacerbated during the COVID-19 pandemic, which forced teaching across education levels to move online (Daniel, 2020 ). Online teaching accelerated the use of digital technologies generating questions regarding the process, the nature, the extent, and the effectiveness of digitalization in schools (Cachia et al., 2021 ; König et al., 2020 ). Specifically, many schools demonstrated a lack of experience and low digital capacity, which resulted in widening gaps, inequalities, and learning losses (Blaskó et al., 2021 ; Di Pietro et al, 2020 ). Such results have engendered the need for schools to learn and build upon the experience in order to enhance their digital capacity (European Commission, 2020 ) and increase their digitalization levels (Costa et al., 2021 ). Digitalization offers possibilities for fundamental improvement in schools (OECD, 2021 ; Rott & Marouane, 2018 ) and touches many aspects of a school’s development (Delcker & Ifenthaler, 2021 ) . However, it is a complex process that requires large-scale transformative changes beyond the technical aspects of technology and infrastructure (Pettersson, 2021 ). Namely, digitalization refers to “ a series of deep and coordinated culture, workforce, and technology shifts and operating models ” (Brooks & McCormack, 2020 , p. 3) that brings cultural, organizational, and operational change through the integration of digital technologies (JISC, 2020 ). A successful digital transformation requires that schools increase their digital capacity levels, establishing the necessary “ culture, policies, infrastructure as well as digital competence of students and staff to support the effective integration of technology in teaching and learning practices ” (Costa et al, 2021 , p.163).

Given that the integration of digital technologies is a complex and continuous process that impacts different actors within the school ecosystem (Eng, 2005 ), there is a need to show how the different elements of the impact are interconnected and to identify the factors that can encourage an effective and efficient change in the school environment. To address the issues outlined above, we formulated the following research questions:

a) What is the impact of digital technologies on education?

b) Which factors might affect a school’s digital capacity and transformation?

In the present investigation, we conducted a non-systematic literature review of publications pertaining to the impact of digital technologies on education and the factors that affect a school’s digital capacity and transformation. The results of the literature review were organized thematically based on the evidence presented about the impact of digital technology on education and the factors which affect the schools’ digital capacity and digital transformation.

Methodology

The non-systematic literature review presented herein covers the main theories and research published over the past 17 years on the topic. It is based on meta-analyses and review papers found in scholarly, peer-reviewed content databases and other key studies and reports related to the concepts studied (e.g., digitalization, digital capacity) from professional and international bodies (e.g., the OECD). We searched the Scopus database, which indexes various online journals in the education sector with an international scope, to collect peer-reviewed academic papers. Furthermore, we used an all-inclusive Google Scholar search to include relevant key terms or to include studies found in the reference list of the peer-reviewed papers, and other key studies and reports related to the concepts studied by professional and international bodies. Lastly, we gathered sources from the Publications Office of the European Union ( https://op.europa.eu/en/home ); namely, documents that refer to policies related to digital transformation in education.

Regarding search terms, we first searched resources on the impact of digital technologies on education by performing the following search queries: “impact” OR “effects” AND “digital technologies” AND “education”, “impact” OR “effects” AND “ICT” AND “education”. We further refined our results by adding the terms “meta-analysis” and “review” or by adjusting the search options based on the features of each database to avoid collecting individual studies that would provide limited contributions to a particular domain. We relied on meta-analyses and review studies as these consider the findings of multiple studies to offer a more comprehensive view of the research in a given area (Schuele & Justice, 2006 ). Specifically, meta-analysis studies provided quantitative evidence based on statistically verifiable results regarding the impact of educational interventions that integrate digital technologies in school classrooms (Higgins et al., 2012 ; Tolani-Brown et al., 2011 ).

However, quantitative data does not offer explanations for the challenges or difficulties experienced during ICT integration in learning and teaching (Tolani-Brown et al., 2011 ). To fill this gap, we analyzed literature reviews and gathered in-depth qualitative evidence of the benefits and implications of technology integration in schools. In the analysis presented herein, we also included policy documents and reports from professional and international bodies and governmental reports, which offered useful explanations of the key concepts of this study and provided recent evidence on digital capacity and transformation in education along with policy recommendations. The inclusion and exclusion criteria that were considered in this study are presented in Table ​ Table1 1 .

Inclusion and exclusion criteria for the selection of resources on the impact of digital technologies on education

To ensure a reliable extraction of information from each study and assist the research synthesis we selected the study characteristics of interest (impact) and constructed coding forms. First, an overview of the synthesis was provided by the principal investigator who described the processes of coding, data entry, and data management. The coders followed the same set of instructions but worked independently. To ensure a common understanding of the process between coders, a sample of ten studies was tested. The results were compared, and the discrepancies were identified and resolved. Additionally, to ensure an efficient coding process, all coders participated in group meetings to discuss additions, deletions, and modifications (Stock, 1994 ). Due to the methodological diversity of the studied documents we began to synthesize the literature review findings based on similar study designs. Specifically, most of the meta-analysis studies were grouped in one category due to the quantitative nature of the measured impact. These studies tended to refer to student achievement (Hattie et al., 2014 ). Then, we organized the themes of the qualitative studies in several impact categories. Lastly, we synthesized both review and meta-analysis data across the categories. In order to establish a collective understanding of the concept of impact, we referred to a previous impact study by Balanskat ( 2009 ) which investigated the impact of technology in primary schools. In this context, the impact had a more specific ICT-related meaning and was described as “ a significant influence or effect of ICT on the measured or perceived quality of (parts of) education ” (Balanskat, 2009 , p. 9). In the study presented herein, the main impacts are in relation to learning and learners, teaching, and teachers, as well as other key stakeholders who are directly or indirectly connected to the school unit.

The study’s results identified multiple dimensions of the impact of digital technologies on students’ knowledge, skills, and attitudes; on equality, inclusion, and social integration; on teachers’ professional and teaching practices; and on other school-related aspects and stakeholders. The data analysis indicated various factors that might affect the schools’ digital capacity and transformation, such as digital competencies, the teachers’ personal characteristics and professional development, as well as the school’s leadership and management, administration, infrastructure, etc. The impacts and factors found in the literature review are presented below.

Impacts of digital technologies on students’ knowledge, skills, attitudes, and emotions

The impact of ICT use on students’ knowledge, skills, and attitudes has been investigated early in the literature. Eng ( 2005 ) found a small positive effect between ICT use and students' learning. Specifically, the author reported that access to computer-assisted instruction (CAI) programs in simulation or tutorial modes—used to supplement rather than substitute instruction – could enhance student learning. The author reported studies showing that teachers acknowledged the benefits of ICT on pupils with special educational needs; however, the impact of ICT on students' attainment was unclear. Balanskat et al. ( 2006 ) found a statistically significant positive association between ICT use and higher student achievement in primary and secondary education. The authors also reported improvements in the performance of low-achieving pupils. The use of ICT resulted in further positive gains for students, namely increased attention, engagement, motivation, communication and process skills, teamwork, and gains related to their behaviour towards learning. Evidence from qualitative studies showed that teachers, students, and parents recognized the positive impact of ICT on students' learning regardless of their competence level (strong/weak students). Punie et al. ( 2006 ) documented studies that showed positive results of ICT-based learning for supporting low-achieving pupils and young people with complex lives outside the education system. Liao et al. ( 2007 ) reported moderate positive effects of computer application instruction (CAI, computer simulations, and web-based learning) over traditional instruction on primary school student's achievement. Similarly, Tamim et al. ( 2011 ) reported small to moderate positive effects between the use of computer technology (CAI, ICT, simulations, computer-based instruction, digital and hypermedia) and student achievement in formal face-to-face classrooms compared to classrooms that did not use technology. Jewitt et al., ( 2011 ) found that the use of learning platforms (LPs) (virtual learning environments, management information systems, communication technologies, and information- and resource-sharing technologies) in schools allowed primary and secondary students to access a wider variety of quality learning resources, engage in independent and personalized learning, and conduct self- and peer-review; LPs also provide opportunities for teacher assessment and feedback. Similar findings were reported by Fu ( 2013 ), who documented a list of benefits and opportunities of ICT use. According to the author, the use of ICTs helps students access digital information and course content effectively and efficiently, supports student-centered and self-directed learning, as well as the development of a creative learning environment where more opportunities for critical thinking skills are offered, and promotes collaborative learning in a distance-learning environment. Higgins et al. ( 2012 ) found consistent but small positive associations between the use of technology and learning outcomes of school-age learners (5–18-year-olds) in studies linking the provision and use of technology with attainment. Additionally, Chauhan ( 2017 ) reported a medium positive effect of technology on the learning effectiveness of primary school students compared to students who followed traditional learning instruction.

The rise of mobile technologies and hardware devices instigated investigations into their impact on teaching and learning. Sung et al. ( 2016 ) reported a moderate effect on students' performance from the use of mobile devices in the classroom compared to the use of desktop computers or the non-use of mobile devices. Schmid et al. ( 2014 ) reported medium–low to low positive effects of technology integration (e.g., CAI, ICTs) in the classroom on students' achievement and attitude compared to not using technology or using technology to varying degrees. Tamim et al. ( 2015 ) found a low statistically significant effect of the use of tablets and other smart devices in educational contexts on students' achievement outcomes. The authors suggested that tablets offered additional advantages to students; namely, they reported improvements in students’ notetaking, organizational and communication skills, and creativity. Zheng et al. ( 2016 ) reported a small positive effect of one-to-one laptop programs on students’ academic achievement across subject areas. Additional reported benefits included student-centered, individualized, and project-based learning enhanced learner engagement and enthusiasm. Additionally, the authors found that students using one-to-one laptop programs tended to use technology more frequently than in non-laptop classrooms, and as a result, they developed a range of skills (e.g., information skills, media skills, technology skills, organizational skills). Haßler et al. ( 2016 ) found that most interventions that included the use of tablets across the curriculum reported positive learning outcomes. However, from 23 studies, five reported no differences, and two reported a negative effect on students' learning outcomes. Similar results were indicated by Kalati and Kim ( 2022 ) who investigated the effect of touchscreen technologies on young students’ learning. Specifically, from 53 studies, 34 advocated positive effects of touchscreen devices on children’s learning, 17 obtained mixed findings and two studies reported negative effects.

More recently, approaches that refer to the impact of gamification with the use of digital technologies on teaching and learning were also explored. A review by Pan et al. ( 2022 ) that examined the role of learning games in fostering mathematics education in K-12 settings, reported that gameplay improved students’ performance. Integration of digital games in teaching was also found as a promising pedagogical practice in STEM education that could lead to increased learning gains (Martinez et al., 2022 ; Wang et al., 2022 ). However, although Talan et al. ( 2020 ) reported a medium effect of the use of educational games (both digital and non-digital) on academic achievement, the effect of non-digital games was higher.

Over the last two years, the effects of more advanced technologies on teaching and learning were also investigated. Garzón and Acevedo ( 2019 ) found that AR applications had a medium effect on students' learning outcomes compared to traditional lectures. Similarly, Garzón et al. ( 2020 ) showed that AR had a medium impact on students' learning gains. VR applications integrated into various subjects were also found to have a moderate effect on students’ learning compared to control conditions (traditional classes, e.g., lectures, textbooks, and multimedia use, e.g., images, videos, animation, CAI) (Chen et al., 2022b ). Villena-Taranilla et al. ( 2022 ) noted the moderate effect of VR technologies on students’ learning when these were applied in STEM disciplines. In the same meta-analysis, Villena-Taranilla et al. ( 2022 ) highlighted the role of immersive VR, since its effect on students’ learning was greater (at a high level) across educational levels (K-6) compared to semi-immersive and non-immersive integrations. In another meta-analysis study, the effect size of the immersive VR was small and significantly differentiated across educational levels (Coban et al., 2022 ). The impact of AI on education was investigated by Su and Yang ( 2022 ) and Su et al. ( 2022 ), who showed that this technology significantly improved students’ understanding of AI computer science and machine learning concepts.

It is worth noting that the vast majority of studies referred to learning gains in specific subjects. Specifically, several studies examined the impact of digital technologies on students’ literacy skills and reported positive effects on language learning (Balanskat et al., 2006 ; Grgurović et al., 2013 ; Friedel et al., 2013 ; Zheng et al., 2016 ; Chen et al., 2022b ; Savva et al., 2022 ). Also, several studies documented positive effects on specific language learning areas, namely foreign language learning (Kao, 2014 ), writing (Higgins et al., 2012 ; Wen & Walters, 2022 ; Zheng et al., 2016 ), as well as reading and comprehension (Cheung & Slavin, 2011 ; Liao et al., 2007 ; Schwabe et al., 2022 ). ICTs were also found to have a positive impact on students' performance in STEM (science, technology, engineering, and mathematics) disciplines (Arztmann et al., 2022 ; Bado, 2022 ; Villena-Taranilla et al., 2022 ; Wang et al., 2022 ). Specifically, a number of studies reported positive impacts on students’ achievement in mathematics (Balanskat et al., 2006 ; Hillmayr et al., 2020 ; Li & Ma, 2010 ; Pan et al., 2022 ; Ran et al., 2022 ; Verschaffel et al., 2019 ; Zheng et al., 2016 ). Furthermore, studies documented positive effects of ICTs on science learning (Balanskat et al., 2006 ; Liao et al., 2007 ; Zheng et al., 2016 ; Hillmayr et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ; Lei et al., 2022a ). Çelik ( 2022 ) also noted that computer simulations can help students understand learning concepts related to science. Furthermore, some studies documented that the use of ICTs had a positive impact on students’ achievement in other subjects, such as geography, history, music, and arts (Chauhan, 2017 ; Condie & Munro, 2007 ), and design and technology (Balanskat et al., 2006 ).

More specific positive learning gains were reported in a number of skills, e.g., problem-solving skills and pattern exploration skills (Higgins et al., 2012 ), metacognitive learning outcomes (Verschaffel et al., 2019 ), literacy skills, computational thinking skills, emotion control skills, and collaborative inquiry skills (Lu et al., 2022 ; Su & Yang, 2022 ; Su et al., 2022 ). Additionally, several investigations have reported benefits from the use of ICT on students’ creativity (Fielding & Murcia, 2022 ; Liu et al., 2022 ; Quah & Ng, 2022 ). Lastly, digital technologies were also found to be beneficial for enhancing students’ lifelong learning skills (Haleem et al., 2022 ).

Apart from gaining knowledge and skills, studies also reported improvement in motivation and interest in mathematics (Higgins et. al., 2019 ; Fadda et al., 2022 ) and increased positive achievement emotions towards several subjects during interventions using educational games (Lei et al., 2022a ). Chen et al. ( 2022a ) also reported a small but positive effect of digital health approaches in bullying and cyberbullying interventions with K-12 students, demonstrating that technology-based approaches can help reduce bullying and related consequences by providing emotional support, empowerment, and change of attitude. In their meta-review study, Su et al. ( 2022 ) also documented that AI technologies effectively strengthened students’ attitudes towards learning. In another meta-analysis, Arztmann et al. ( 2022 ) reported positive effects of digital games on motivation and behaviour towards STEM subjects.

Impacts of digital technologies on equality, inclusion and social integration

Although most of the reviewed studies focused on the impact of ICTs on students’ knowledge, skills, and attitudes, reports were also made on other aspects in the school context, such as equality, inclusion, and social integration. Condie and Munro ( 2007 ) documented research interventions investigating how ICT can support pupils with additional or special educational needs. While those interventions were relatively small scale and mostly based on qualitative data, their findings indicated that the use of ICTs enabled the development of communication, participation, and self-esteem. A recent meta-analysis (Baragash et al., 2022 ) with 119 participants with different disabilities, reported a significant overall effect size of AR on their functional skills acquisition. Koh’s meta-analysis ( 2022 ) also revealed that students with intellectual and developmental disabilities improved their competence and performance when they used digital games in the lessons.

Istenic Starcic and Bagon ( 2014 ) found that the role of ICT in inclusion and the design of pedagogical and technological interventions was not sufficiently explored in educational interventions with people with special needs; however, some benefits of ICT use were found in students’ social integration. The issue of gender and technology use was mentioned in a small number of studies. Zheng et al. ( 2016 ) reported a statistically significant positive interaction between one-to-one laptop programs and gender. Specifically, the results showed that girls and boys alike benefitted from the laptop program, but the effect on girls’ achievement was smaller than that on boys’. Along the same lines, Arztmann et al. ( 2022 ) reported no difference in the impact of game-based learning between boys and girls, arguing that boys and girls equally benefited from game-based interventions in STEM domains. However, results from a systematic review by Cussó-Calabuig et al. ( 2018 ) found limited and low-quality evidence on the effects of intensive use of computers on gender differences in computer anxiety, self-efficacy, and self-confidence. Based on their view, intensive use of computers can reduce gender differences in some areas and not in others, depending on contextual and implementation factors.

Impacts of digital technologies on teachers’ professional and teaching practices

Various research studies have explored the impact of ICT on teachers’ instructional practices and student assessment. Friedel et al. ( 2013 ) found that the use of mobile devices by students enabled teachers to successfully deliver content (e.g., mobile serious games), provide scaffolding, and facilitate synchronous collaborative learning. The integration of digital games in teaching and learning activities also gave teachers the opportunity to study and apply various pedagogical practices (Bado, 2022 ). Specifically, Bado ( 2022 ) found that teachers who implemented instructional activities in three stages (pre-game, game, and post-game) maximized students’ learning outcomes and engagement. For instance, during the pre-game stage, teachers focused on lectures and gameplay training, at the game stage teachers provided scaffolding on content, addressed technical issues, and managed the classroom activities. During the post-game stage, teachers organized activities for debriefing to ensure that the gameplay had indeed enhanced students’ learning outcomes.

Furthermore, ICT can increase efficiency in lesson planning and preparation by offering possibilities for a more collaborative approach among teachers. The sharing of curriculum plans and the analysis of students’ data led to clearer target settings and improvements in reporting to parents (Balanskat et al., 2006 ).

Additionally, the use and application of digital technologies in teaching and learning were found to enhance teachers’ digital competence. Balanskat et al. ( 2006 ) documented studies that revealed that the use of digital technologies in education had a positive effect on teachers’ basic ICT skills. The greatest impact was found on teachers with enough experience in integrating ICTs in their teaching and/or who had recently participated in development courses for the pedagogical use of technologies in teaching. Punie et al. ( 2006 ) reported that the provision of fully equipped multimedia portable computers and the development of online teacher communities had positive impacts on teachers’ confidence and competence in the use of ICTs.

Moreover, online assessment via ICTs benefits instruction. In particular, online assessments support the digitalization of students’ work and related logistics, allow teachers to gather immediate feedback and readjust to new objectives, and support the improvement of the technical quality of tests by providing more accurate results. Additionally, the capabilities of ICTs (e.g., interactive media, simulations) create new potential methods of testing specific skills, such as problem-solving and problem-processing skills, meta-cognitive skills, creativity and communication skills, and the ability to work productively in groups (Punie et al., 2006 ).

Impacts of digital technologies on other school-related aspects and stakeholders

There is evidence that the effective use of ICTs and the data transmission offered by broadband connections help improve administration (Balanskat et al., 2006 ). Specifically, ICTs have been found to provide better management systems to schools that have data gathering procedures in place. Condie and Munro ( 2007 ) reported impacts from the use of ICTs in schools in the following areas: attendance monitoring, assessment records, reporting to parents, financial management, creation of repositories for learning resources, and sharing of information amongst staff. Such data can be used strategically for self-evaluation and monitoring purposes which in turn can result in school improvements. Additionally, they reported that online access to other people with similar roles helped to reduce headteachers’ isolation by offering them opportunities to share insights into the use of ICT in learning and teaching and how it could be used to support school improvement. Furthermore, ICTs provided more efficient and successful examination management procedures, namely less time-consuming reporting processes compared to paper-based examinations and smooth communications between schools and examination authorities through electronic data exchange (Punie et al., 2006 ).

Zheng et al. ( 2016 ) reported that the use of ICTs improved home-school relationships. Additionally, Escueta et al. ( 2017 ) reported several ICT programs that had improved the flow of information from the school to parents. Particularly, they documented that the use of ICTs (learning management systems, emails, dedicated websites, mobile phones) allowed for personalized and customized information exchange between schools and parents, such as attendance records, upcoming class assignments, school events, and students’ grades, which generated positive results on students’ learning outcomes and attainment. Such information exchange between schools and families prompted parents to encourage their children to put more effort into their schoolwork.

The above findings suggest that the impact of ICT integration in schools goes beyond students’ performance in school subjects. Specifically, it affects a number of school-related aspects, such as equality and social integration, professional and teaching practices, and diverse stakeholders. In Table ​ Table2, 2 , we summarize the different impacts of digital technologies on school stakeholders based on the literature review, while in Table ​ Table3 3 we organized the tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript.

The impact of digital technologies on schools’ stakeholders based on the literature review

Tools/platforms and practices/policies addressed in the meta-analyses, literature reviews, EU reports, and international bodies included in the manuscript

Additionally, based on the results of the literature review, there are many types of digital technologies with different affordances (see, for example, studies on VR vs Immersive VR), which evolve over time (e.g. starting from CAIs in 2005 to Augmented and Virtual reality 2020). Furthermore, these technologies are linked to different pedagogies and policy initiatives, which are critical factors in the study of impact. Table ​ Table3 3 summarizes the different tools and practices that have been used to examine the impact of digital technologies on education since 2005 based on the review results.

Factors that affect the integration of digital technologies

Although the analysis of the literature review demonstrated different impacts of the use of digital technology on education, several authors highlighted the importance of various factors, besides the technology itself, that affect this impact. For example, Liao et al. ( 2007 ) suggested that future studies should carefully investigate which factors contribute to positive outcomes by clarifying the exact relationship between computer applications and learning. Additionally, Haßler et al., ( 2016 ) suggested that the neutral findings regarding the impact of tablets on students learning outcomes in some of the studies included in their review should encourage educators, school leaders, and school officials to further investigate the potential of such devices in teaching and learning. Several other researchers suggested that a number of variables play a significant role in the impact of ICTs on students’ learning that could be attributed to the school context, teaching practices and professional development, the curriculum, and learners’ characteristics (Underwood, 2009 ; Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Tang et al., 2022 ).

Digital competencies

One of the most common challenges reported in studies that utilized digital tools in the classroom was the lack of students’ skills on how to use them. Fu ( 2013 ) found that students’ lack of technical skills is a barrier to the effective use of ICT in the classroom. Tamim et al. ( 2015 ) reported that students faced challenges when using tablets and smart mobile devices, associated with the technical issues or expertise needed for their use and the distracting nature of the devices and highlighted the need for teachers’ professional development. Higgins et al. ( 2012 ) reported that skills training about the use of digital technologies is essential for learners to fully exploit the benefits of instruction.

Delgado et al. ( 2015 ), meanwhile, reported studies that showed a strong positive association between teachers’ computer skills and students’ use of computers. Teachers’ lack of ICT skills and familiarization with technologies can become a constraint to the effective use of technology in the classroom (Balanskat et al., 2006 ; Delgado et al., 2015 ).

It is worth noting that the way teachers are introduced to ICTs affects the impact of digital technologies on education. Previous studies have shown that teachers may avoid using digital technologies due to limited digital skills (Balanskat, 2006 ), or they prefer applying “safe” technologies, namely technologies that their own teachers used and with which they are familiar (Condie & Munro, 2007 ). In this regard, the provision of digital skills training and exposure to new digital tools might encourage teachers to apply various technologies in their lessons (Condie & Munro, 2007 ). Apart from digital competence, technical support in the school setting has also been shown to affect teachers’ use of technology in their classrooms (Delgado et al., 2015 ). Ferrari et al. ( 2011 ) found that while teachers’ use of ICT is high, 75% stated that they needed more institutional support and a shift in the mindset of educational actors to achieve more innovative teaching practices. The provision of support can reduce time and effort as well as cognitive constraints, which could cause limited ICT integration in the school lessons by teachers (Escueta et al., 2017 ).

Teachers’ personal characteristics, training approaches, and professional development

Teachers’ personal characteristics and professional development affect the impact of digital technologies on education. Specifically, Cheok and Wong ( 2015 ) found that teachers’ personal characteristics (e.g., anxiety, self-efficacy) are associated with their satisfaction and engagement with technology. Bingimlas ( 2009 ) reported that lack of confidence, resistance to change, and negative attitudes in using new technologies in teaching are significant determinants of teachers’ levels of engagement in ICT. The same author reported that the provision of technical support, motivation support (e.g., awards, sufficient time for planning), and training on how technologies can benefit teaching and learning can eliminate the above barriers to ICT integration. Archer et al. ( 2014 ) found that comfort levels in using technology are an important predictor of technology integration and argued that it is essential to provide teachers with appropriate training and ongoing support until they are comfortable with using ICTs in the classroom. Hillmayr et al. ( 2020 ) documented that training teachers on ICT had an important effecton students’ learning.

According to Balanskat et al. ( 2006 ), the impact of ICTs on students’ learning is highly dependent on the teachers’ capacity to efficiently exploit their application for pedagogical purposes. Results obtained from the Teaching and Learning International Survey (TALIS) (OECD, 2021 ) revealed that although schools are open to innovative practices and have the capacity to adopt them, only 39% of teachers in the European Union reported that they are well or very well prepared to use digital technologies for teaching. Li and Ma ( 2010 ) and Hardman ( 2019 ) showed that the positive effect of technology on students’ achievement depends on the pedagogical practices used by teachers. Schmid et al. ( 2014 ) reported that learning was best supported when students were engaged in active, meaningful activities with the use of technological tools that provided cognitive support. Tamim et al. ( 2015 ) compared two different pedagogical uses of tablets and found a significant moderate effect when the devices were used in a student-centered context and approach rather than within teacher-led environments. Similarly, Garzón and Acevedo ( 2019 ) and Garzón et al. ( 2020 ) reported that the positive results from the integration of AR applications could be attributed to the existence of different variables which could influence AR interventions (e.g., pedagogical approach, learning environment, and duration of the intervention). Additionally, Garzón et al. ( 2020 ) suggested that the pedagogical resources that teachers used to complement their lectures and the pedagogical approaches they applied were crucial to the effective integration of AR on students’ learning gains. Garzón and Acevedo ( 2019 ) also emphasized that the success of a technology-enhanced intervention is based on both the technology per se and its characteristics and on the pedagogical strategies teachers choose to implement. For instance, their results indicated that the collaborative learning approach had the highest impact on students’ learning gains among other approaches (e.g., inquiry-based learning, situated learning, or project-based learning). Ran et al. ( 2022 ) also found that the use of technology to design collaborative and communicative environments showed the largest moderator effects among the other approaches.

Hattie ( 2008 ) reported that the effective use of computers is associated with training teachers in using computers as a teaching and learning tool. Zheng et al. ( 2016 ) noted that in addition to the strategies teachers adopt in teaching, ongoing professional development is also vital in ensuring the success of technology implementation programs. Sung et al. ( 2016 ) found that research on the use of mobile devices to support learning tends to report that the insufficient preparation of teachers is a major obstacle in implementing effective mobile learning programs in schools. Friedel et al. ( 2013 ) found that providing training and support to teachers increased the positive impact of the interventions on students’ learning gains. Trucano ( 2005 ) argued that positive impacts occur when digital technologies are used to enhance teachers’ existing pedagogical philosophies. Higgins et al. ( 2012 ) found that the types of technologies used and how they are used could also affect students’ learning. The authors suggested that training and professional development of teachers that focuses on the effective pedagogical use of technology to support teaching and learning is an important component of successful instructional approaches (Higgins et al., 2012 ). Archer et al. ( 2014 ) found that studies that reported ICT interventions during which teachers received training and support had moderate positive effects on students’ learning outcomes, which were significantly higher than studies where little or no detail about training and support was mentioned. Fu ( 2013 ) reported that the lack of teachers’ knowledge and skills on the technical and instructional aspects of ICT use in the classroom, in-service training, pedagogy support, technical and financial support, as well as the lack of teachers’ motivation and encouragement to integrate ICT on their teaching were significant barriers to the integration of ICT in education.

School leadership and management

Management and leadership are important cornerstones in the digital transformation process (Pihir et al., 2018 ). Zheng et al. ( 2016 ) documented leadership among the factors positively affecting the successful implementation of technology integration in schools. Strong leadership, strategic planning, and systematic integration of digital technologies are prerequisites for the digital transformation of education systems (Ređep, 2021 ). Management and leadership play a significant role in formulating policies that are translated into practice and ensure that developments in ICT become embedded into the life of the school and in the experiences of staff and pupils (Condie & Munro, 2007 ). Policy support and leadership must include the provision of an overall vision for the use of digital technologies in education, guidance for students and parents, logistical support, as well as teacher training (Conrads et al., 2017 ). Unless there is a commitment throughout the school, with accountability for progress at key points, it is unlikely for ICT integration to be sustained or become part of the culture (Condie & Munro, 2007 ). To achieve this, principals need to adopt and promote a whole-institution strategy and build a strong mutual support system that enables the school’s technological maturity (European Commission, 2019 ). In this context, school culture plays an essential role in shaping the mindsets and beliefs of school actors towards successful technology integration. Condie and Munro ( 2007 ) emphasized the importance of the principal’s enthusiasm and work as a source of inspiration for the school staff and the students to cultivate a culture of innovation and establish sustainable digital change. Specifically, school leaders need to create conditions in which the school staff is empowered to experiment and take risks with technology (Elkordy & Lovinelli, 2020 ).

In order for leaders to achieve the above, it is important to develop capacities for learning and leading, advocating professional learning, and creating support systems and structures (European Commission, 2019 ). Digital technology integration in education systems can be challenging and leadership needs guidance to achieve it. Such guidance can be introduced through the adoption of new methods and techniques in strategic planning for the integration of digital technologies (Ređep, 2021 ). Even though the role of leaders is vital, the relevant training offered to them has so far been inadequate. Specifically, only a third of the education systems in Europe have put in place national strategies that explicitly refer to the training of school principals (European Commission, 2019 , p. 16).

Connectivity, infrastructure, and government and other support

The effective integration of digital technologies across levels of education presupposes the development of infrastructure, the provision of digital content, and the selection of proper resources (Voogt et al., 2013 ). Particularly, a high-quality broadband connection in the school increases the quality and quantity of educational activities. There is evidence that ICT increases and formalizes cooperative planning between teachers and cooperation with managers, which in turn has a positive impact on teaching practices (Balanskat et al., 2006 ). Additionally, ICT resources, including software and hardware, increase the likelihood of teachers integrating technology into the curriculum to enhance their teaching practices (Delgado et al., 2015 ). For example, Zheng et al. ( 2016 ) found that the use of one-on-one laptop programs resulted in positive changes in teaching and learning, which would not have been accomplished without the infrastructure and technical support provided to teachers. Delgado et al. ( 2015 ) reported that limited access to technology (insufficient computers, peripherals, and software) and lack of technical support are important barriers to ICT integration. Access to infrastructure refers not only to the availability of technology in a school but also to the provision of a proper amount and the right types of technology in locations where teachers and students can use them. Effective technical support is a central element of the whole-school strategy for ICT (Underwood, 2009 ). Bingimlas ( 2009 ) reported that lack of technical support in the classroom and whole-school resources (e.g., failing to connect to the Internet, printers not printing, malfunctioning computers, and working on old computers) are significant barriers that discourage the use of ICT by teachers. Moreover, poor quality and inadequate hardware maintenance, and unsuitable educational software may discourage teachers from using ICTs (Balanskat et al., 2006 ; Bingimlas, 2009 ).

Government support can also impact the integration of ICTs in teaching. Specifically, Balanskat et al. ( 2006 ) reported that government interventions and training programs increased teachers’ enthusiasm and positive attitudes towards ICT and led to the routine use of embedded ICT.

Lastly, another important factor affecting digital transformation is the development and quality assurance of digital learning resources. Such resources can be support textbooks and related materials or resources that focus on specific subjects or parts of the curriculum. Policies on the provision of digital learning resources are essential for schools and can be achieved through various actions. For example, some countries are financing web portals that become repositories, enabling teachers to share resources or create their own. Additionally, they may offer e-learning opportunities or other services linked to digital education. In other cases, specific agencies of projects have also been set up to develop digital resources (Eurydice, 2019 ).

Administration and digital data management

The digital transformation of schools involves organizational improvements at the level of internal workflows, communication between the different stakeholders, and potential for collaboration. Vuorikari et al. ( 2020 ) presented evidence that digital technologies supported the automation of administrative practices in schools and reduced the administration’s workload. There is evidence that digital data affects the production of knowledge about schools and has the power to transform how schooling takes place. Specifically, Sellar ( 2015 ) reported that data infrastructure in education is developing due to the demand for “ information about student outcomes, teacher quality, school performance, and adult skills, associated with policy efforts to increase human capital and productivity practices ” (p. 771). In this regard, practices, such as datafication which refers to the “ translation of information about all kinds of things and processes into quantified formats” have become essential for decision-making based on accountability reports about the school’s quality. The data could be turned into deep insights about education or training incorporating ICTs. For example, measuring students’ online engagement with the learning material and drawing meaningful conclusions can allow teachers to improve their educational interventions (Vuorikari et al., 2020 ).

Students’ socioeconomic background and family support

Research show that the active engagement of parents in the school and their support for the school’s work can make a difference to their children’s attitudes towards learning and, as a result, their achievement (Hattie, 2008 ). In recent years, digital technologies have been used for more effective communication between school and family (Escueta et al., 2017 ). The European Commission ( 2020 ) presented data from a Eurostat survey regarding the use of computers by students during the pandemic. The data showed that younger pupils needed additional support and guidance from parents and the challenges were greater for families in which parents had lower levels of education and little to no digital skills.

In this regard, the socio-economic background of the learners and their socio-cultural environment also affect educational achievements (Punie et al., 2006 ). Trucano documented that the use of computers at home positively influenced students’ confidence and resulted in more frequent use at school, compared to students who had no home access (Trucano, 2005 ). In this sense, the socio-economic background affects the access to computers at home (OECD, 2015 ) which in turn influences the experience of ICT, an important factor for school achievement (Punie et al., 2006 ; Underwood, 2009 ). Furthermore, parents from different socio-economic backgrounds may have different abilities and availability to support their children in their learning process (Di Pietro et al., 2020 ).

Schools’ socioeconomic context and emergency situations

The socio-economic context of the school is closely related to a school’s digital transformation. For example, schools in disadvantaged, rural, or deprived areas are likely to lack the digital capacity and infrastructure required to adapt to the use of digital technologies during emergency periods, such as the COVID-19 pandemic (Di Pietro et al., 2020 ). Data collected from school principals confirmed that in several countries, there is a rural/urban divide in connectivity (OECD, 2015 ).

Emergency periods also affect the digitalization of schools. The COVID-19 pandemic led to the closure of schools and forced them to seek appropriate and connective ways to keep working on the curriculum (Di Pietro et al., 2020 ). The sudden large-scale shift to distance and online teaching and learning also presented challenges around quality and equity in education, such as the risk of increased inequalities in learning, digital, and social, as well as teachers facing difficulties coping with this demanding situation (European Commission, 2020 ).

Looking at the findings of the above studies, we can conclude that the impact of digital technologies on education is influenced by various actors and touches many aspects of the school ecosystem. Figure  1 summarizes the factors affecting the digital technologies’ impact on school stakeholders based on the findings from the literature review.

Factors that affect the impact of ICTs on education

The findings revealed that the use of digital technologies in education affects a variety of actors within a school’s ecosystem. First, we observed that as technologies evolve, so does the interest of the research community to apply them to school settings. Figure  2 summarizes the trends identified in current research around the impact of digital technologies on schools’ digital capacity and transformation as found in the present study. Starting as early as 2005, when computers, simulations, and interactive boards were the most commonly applied tools in school interventions (e.g., Eng, 2005 ; Liao et al., 2007 ; Moran et al., 2008 ; Tamim et al., 2011 ), moving towards the use of learning platforms (Jewitt et al., 2011 ), then to the use of mobile devices and digital games (e.g., Tamim et al., 2015 ; Sung et al., 2016 ; Talan et al., 2020 ), as well as e-books (e.g., Savva et al., 2022 ), to the more recent advanced technologies, such as AR and VR applications (e.g., Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Kalemkuş & Kalemkuş, 2022 ), or robotics and AI (e.g., Su & Yang, 2022 ; Su et al., 2022 ). As this evolution shows, digital technologies are a concept in flux with different affordances and characteristics. Additionally, from an instructional perspective, there has been a growing interest in different modes and models of content delivery such as online, blended, and hybrid modes (e.g., Cheok & Wong, 2015 ; Kazu & Yalçin, 2022 ; Ulum, 2022 ). This is an indication that the value of technologies to support teaching and learning as well as other school-related practices is increasingly recognized by the research and school community. The impact results from the literature review indicate that ICT integration on students’ learning outcomes has effects that are small (Coban et al., 2022 ; Eng, 2005 ; Higgins et al., 2012 ; Schmid et al., 2014 ; Tamim et al., 2015 ; Zheng et al., 2016 ) to moderate (Garzón & Acevedo, 2019 ; Garzón et al., 2020 ; Liao et al., 2007 ; Sung et al., 2016 ; Talan et al., 2020 ; Wen & Walters, 2022 ). That said, a number of recent studies have reported high effect sizes (e.g., Kazu & Yalçin, 2022 ).

Current work and trends in the study of the impact of digital technologies on schools’ digital capacity

Based on these findings, several authors have suggested that the impact of technology on education depends on several variables and not on the technology per se (Tamim et al., 2011 ; Higgins et al., 2012 ; Archer et al., 2014 ; Sung et al., 2016 ; Haßler et al., 2016 ; Chauhan, 2017 ; Lee et al., 2020 ; Lei et al., 2022a ). While the impact of ICTs on student achievement has been thoroughly investigated by researchers, other aspects related to school life that are also affected by ICTs, such as equality, inclusion, and social integration have received less attention. Further analysis of the literature review has revealed a greater investment in ICT interventions to support learning and teaching in the core subjects of literacy and STEM disciplines, especially mathematics, and science. These were the most common subjects studied in the reviewed papers often drawing on national testing results, while studies that investigated other subject areas, such as social studies, were limited (Chauhan, 2017 ; Condie & Munro, 2007 ). As such, research is still lacking impact studies that focus on the effects of ICTs on a range of curriculum subjects.

The qualitative research provided additional information about the impact of digital technologies on education, documenting positive effects and giving more details about implications, recommendations, and future research directions. Specifically, the findings regarding the role of ICTs in supporting learning highlight the importance of teachers’ instructional practice and the learning context in the use of technologies and consequently their impact on instruction (Çelik, 2022 ; Schmid et al., 2014 ; Tamim et al., 2015 ). The review also provided useful insights regarding the various factors that affect the impact of digital technologies on education. These factors are interconnected and play a vital role in the transformation process. Specifically, these factors include a) digital competencies; b) teachers’ personal characteristics and professional development; c) school leadership and management; d) connectivity, infrastructure, and government support; e) administration and data management practices; f) students’ socio-economic background and family support and g) the socioeconomic context of the school and emergency situations. It is worth noting that we observed factors that affect the integration of ICTs in education but may also be affected by it. For example, the frequent use of ICTs and the use of laptops by students for instructional purposes positively affect the development of digital competencies (Zheng et al., 2016 ) and at the same time, the digital competencies affect the use of ICTs (Fu, 2013 ; Higgins et al., 2012 ). As a result, the impact of digital technologies should be explored more as an enabler of desirable and new practices and not merely as a catalyst that improves the output of the education process i.e. namely student attainment.

Conclusions

Digital technologies offer immense potential for fundamental improvement in schools. However, investment in ICT infrastructure and professional development to improve school education are yet to provide fruitful results. Digital transformation is a complex process that requires large-scale transformative changes that presuppose digital capacity and preparedness. To achieve such changes, all actors within the school’s ecosystem need to share a common vision regarding the integration of ICTs in education and work towards achieving this goal. Our literature review, which synthesized quantitative and qualitative data from a list of meta-analyses and review studies, provided useful insights into the impact of ICTs on different school stakeholders and showed that the impact of digital technologies touches upon many different aspects of school life, which are often overlooked when the focus is on student achievement as the final output of education. Furthermore, the concept of digital technologies is a concept in flux as technologies are not only different among them calling for different uses in the educational practice but they also change through time. Additionally, we opened a forum for discussion regarding the factors that affect a school’s digital capacity and transformation. We hope that our study will inform policy, practice, and research and result in a paradigm shift towards more holistic approaches in impact and assessment studies.

Study limitations and future directions

We presented a review of the study of digital technologies' impact on education and factors influencing schools’ digital capacity and transformation. The study results were based on a non-systematic literature review grounded on the acquisition of documentation in specific databases. Future studies should investigate more databases to corroborate and enhance our results. Moreover, search queries could be enhanced with key terms that could provide additional insights about the integration of ICTs in education, such as “policies and strategies for ICT integration in education”. Also, the study drew information from meta-analyses and literature reviews to acquire evidence about the effects of ICT integration in schools. Such evidence was mostly based on the general conclusions of the studies. It is worth mentioning that, we located individual studies which showed different, such as negative or neutral results. Thus, further insights are needed about the impact of ICTs on education and the factors influencing the impact. Furthermore, the nature of the studies included in meta-analyses and reviews is different as they are based on different research methodologies and data gathering processes. For instance, in a meta-analysis, the impact among the studies investigated is measured in a particular way, depending on policy or research targets (e.g., results from national examinations, pre-/post-tests). Meanwhile, in literature reviews, qualitative studies offer additional insights and detail based on self-reports and research opinions on several different aspects and stakeholders who could affect and be affected by ICT integration. As a result, it was challenging to draw causal relationships between so many interrelating variables.

Despite the challenges mentioned above, this study envisaged examining school units as ecosystems that consist of several actors by bringing together several variables from different research epistemologies to provide an understanding of the integration of ICTs. However, the use of other tools and methodologies and models for evaluation of the impact of digital technologies on education could give more detailed data and more accurate results. For instance, self-reflection tools, like SELFIE—developed on the DigCompOrg framework- (Kampylis et al., 2015 ; Bocconi & Lightfoot, 2021 ) can help capture a school’s digital capacity and better assess the impact of ICTs on education. Furthermore, the development of a theory of change could be a good approach for documenting the impact of digital technologies on education. Specifically, theories of change are models used for the evaluation of interventions and their impact; they are developed to describe how interventions will work and give the desired outcomes (Mayne, 2015 ). Theory of change as a methodological approach has also been used by researchers to develop models for evaluation in the field of education (e.g., Aromatario et al., 2019 ; Chapman & Sammons, 2013 ; De Silva et al., 2014 ).

We also propose that future studies aim at similar investigations by applying more holistic approaches for impact assessment that can provide in-depth data about the impact of digital technologies on education. For instance, future studies could focus on different research questions about the technologies that are used during the interventions or the way the implementation takes place (e.g., What methodologies are used for documenting impact? How are experimental studies implemented? How can teachers be taken into account and trained on the technology and its functions? What are the elements of an appropriate and successful implementation? How is the whole intervention designed? On which learning theories is the technology implementation based?).

Future research could also focus on assessing the impact of digital technologies on various other subjects since there is a scarcity of research related to particular subjects, such as geography, history, arts, music, and design and technology. More research should also be done about the impact of ICTs on skills, emotions, and attitudes, and on equality, inclusion, social interaction, and special needs education. There is also a need for more research about the impact of ICTs on administration, management, digitalization, and home-school relationships. Additionally, although new forms of teaching and learning with the use of ICTs (e.g., blended, hybrid, and online learning) have initiated several investigations in mainstream classrooms, only a few studies have measured their impact on students’ learning. Additionally, our review did not document any study about the impact of flipped classrooms on K-12 education. Regarding teaching and learning approaches, it is worth noting that studies referred to STEM or STEAM did not investigate the impact of STEM/STEAM as an interdisciplinary approach to learning but only investigated the impact of ICTs on learning in each domain as a separate subject (science, technology, engineering, arts, mathematics). Hence, we propose future research to also investigate the impact of the STEM/STEAM approach on education. The impact of emerging technologies on education, such as AR, VR, robotics, and AI has also been investigated recently, but more work needs to be done.

Finally, we propose that future studies could focus on the way in which specific factors, e.g., infrastructure and government support, school leadership and management, students’ and teachers’ digital competencies, approaches teachers utilize in the teaching and learning (e.g., blended, online and hybrid learning, flipped classrooms, STEM/STEAM approach, project-based learning, inquiry-based learning), affect the impact of digital technologies on education. We hope that future studies will give detailed insights into the concept of schools’ digital transformation through further investigation of impacts and factors which influence digital capacity and transformation based on the results and the recommendations of the present study.

Acknowledgements

This project has received funding under Grant Agreement No Ref Ares (2021) 339036 7483039 as well as funding from the European Union’s Horizon 2020 Research and Innovation Program under Grant Agreement No 739578 and the Government of the Republic of Cyprus through the Deputy Ministry of Research, Innovation and Digital Policy. The UVa co-authors would like also to acknowledge funding from the European Regional Development Fund and the National Research Agency of the Spanish Ministry of Science and Innovation, under project grant PID2020-112584RB-C32.

Data availability statement

Declarations.

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MIT Technology Review

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Technology is probably changing us for the worse—or so we always think

For nearly a hundred years in this publication (and long before that elsewhere) people have worried that new technologies could alter what it means to be human.

• Timothy Maher archive page

MIT Technology Review is celebrating our 125th anniversary with an online series that draws lessons for the future from our past coverage of technology. 

Do we use technology, or does it use us? Do our gadgets improve our lives or just make us weak, lazy, and dumb? These are old questions—maybe older than you think. You’re probably familiar with the way alarmed grown-ups through the decades have assailed the mind-rotting potential of search engines , video games , television , and radio —but those are just the recent examples.

Early in the last century, pundits argued that the telephone severed the need for personal contact and would lead to social isolation. In the 19th century some warned that the bicycle would rob women of their femininity and result in a haggard look known as “bicycle face.” Mary Shelley’s 1818 novel Frankenstein was a warning against using technology to play God, and how it might blur the lines between what’s human and what isn’t.

Or to go back even further: in Plato’s Phaedrus , from around 370 BCE, Socrates suggests that writing could be a detriment to human memory—the argument being, if you’ve written it down, you no longer needed to remember it.

We’ve always greeted new technologies with a mixture of fascination and fear,  says Margaret O’Mara , a historian at the University of Washington who focuses on the intersection of technology and American politics. “People think: ‘Wow, this is going to change everything affirmatively, positively,’” she says. “And at the same time: ‘It’s scary—this is going to corrupt us or change us in some negative way.’”

And then something interesting happens: “We get used to it,” she says. “The novelty wears off and the new thing becomes a habit.” 

A curious fact

Here at MIT Technology Review , writers have grappled with the effects, real or imagined, of tech on the human mind for nearly a hundred years. In our March 1931 issue , in his essay “Machine-Made Minds,” author John Bakeless wrote that it was time to ask “how far the machine’s control over us is a danger calling for vigorous resistance; and how far it is a good thing, to which we may willingly yield.” 

The advances that alarmed him might seem, to us, laughably low-tech: radio transmitters, antennas, or even rotary printing presses.

But Bakeless, who’d published books on Lewis and Clark and other early American explorers, wanted to know not just what the machine age was doing to society but what it was doing to individual people. “It is a curious fact,” he wrote, “that the writers who have dealt with the social, economic, and political effects of the machine have neglected the most important effect of all—its profound influence on the human mind.”

In particular, he was worried about how technology was being used by the media to control what people thought and talked about. 

“Consider the mental equipment of the average modern man,” he wrote. “Most of the raw material of his thought enters his mind by way of a machine of some kind … the Twentieth Century journalist can collect, print, and distribute his news with a speed and completeness wholly due to a score or more of intricate machines … For the first time, thanks to machinery, such a thing as a world-wide public opinion is becoming possible.”

Bakeless didn’t see this as an especially positive development. “Machines are so expensive that the machine-made press is necessarily controlled by a few very wealthy men, who with the very best intentions in the world are still subject to human limitation and the prejudices of their kind … Today the man or the government that controls two machines—wireless and cable—can control the ideas and passions of a continent.”

Fifty years later, the debate had shifted more in the direction of silicon chips. In our October 1980 issue , engineering professor Thomas B. Sheridan, in “Computer Control and Human Alienation,” asked: “How can we ensure that the future computerized society will offer humanity and dignity?” A few years later, in our August/September 1987 issue , writer David Lyon felt he had the answer—we couldn’t, and wouldn’t. In “Hey You! Make Way for My Technology,” he wrote that gadgets like the telephone answering machine and the boom box merely kept other pesky humans at a safe distance: “As machines multiply our capacity to perform useful tasks, they boost our aptitude for thoughtless and self-centered action. Civilized behavior is predicated on the principle of one human being interacting with another, not a human being interacting with a mechanical or electronic extension of another person.”

By this century the subject had been taken up by a pair of celebrities, novelist Jonathan Franzen and Talking Heads lead vocalist David Byrne. In our September/October 2008 issue, Franzen suggested that cell phones had turned us into performance artists. 

In “I Just Called to Say I Love You,” he wrote: “When I’m buying those socks at the Gap and the mom in line behind me shouts ‘I love you!’ into her little phone, I am powerless not to feel that something is being performed; overperformed; publicly performed; defiantly inflicted. Yes, a lot of domestic things get shouted in public which really aren’t intended for public consumption; yes, people get carried away. But the phrase ‘I love you’ is too important and loaded, and its use as a sign-off too self-conscious, for me to believe I’m being made to hear it accidentally.”

In “Eliminating the Human,” from our September/October 2017 issue, Byrne observed that advances in the digital economy served largely to free us from dealing with other people. You could now “keep in touch” with friends without ever seeing them; buy books without interacting with a store clerk; take an online course without ever meeting the teacher or having any awareness of the other students.

“For us as a society, less contact and interaction—real interaction—would seem to lead to less tolerance and understanding of difference, as well as more envy and antagonism,” Byrne wrote. “As has been in evidence recently, social media actually increases divisions by amplifying echo effects and allowing us to live in cognitive bubbles … When interaction becomes a strange and unfamiliar thing, then we will have changed who and what we are as a species.”

Modern woes

It hasn’t stopped. Just last year our own Will Douglas Heaven’s feature on ChatGPT debunked the idea that the AI revolution will destroy children’s ability to develop critical-thinking skills.

As O’Mara puts it: “Do all of the fears of these moral panics come to pass? No. Does change come to pass? Yes.” The way we come to grips with new technologies hasn’t fundamentally changed, she says, but what has changed is—there’s more of it to deal with. “It’s more of the same,” she says. “But it’s more. Digital technologies have allowed things to scale up into a runaway train of sorts that the 19 th century never had to contend with.”

Maybe the problem isn’t technology at all, maybe it’s us. Based on what you might read in 19th-century novels, people haven’t changed much since the early days of the industrial age. In any Dostoyevsky novel you can find people who yearn to be seen as different or special, who take affront at any threat to their carefully curated public persona, who feel depressed and misunderstood and isolated, who are susceptible to mob mentality.

“The biology of the human brain hasn’t changed in the last 250 years,” O’Mara says. “Same neurons, still the same arrangement. But it’s been presented with all these new inputs … I feel like I live with information overload all the time. I think we all observe it in our own lives, how our attention spans just go sideways. But that doesn’t mean my brain has changed at all. We’re just getting used to consuming information in a different way.”

And if you find technology to be intrusive and unavoidable now, it might be useful to note that Bakeless felt no differently in 1931. Even then, long before anyone had heard of smartphone or the internet, he felt that technology had become so intrinsic to daily life that it was like a tyrant: “Even as a despot, the machine is benevolent; and it is after all our stupidity that permits inanimate iron to be a despot at all.”

If we are to ever create the ideal human society, he concluded—one with sufficient time for music, art, philosophy, scientific inquiry (“the gorgeous playthings of the mind,” as he put it)—it was unlikely we’d get it done without the aid of machines. It was too late, we’d already grown too accustomed to the new toys. We just needed to find a way to make sure that the machines served us instead of the other way around. “If we are to build a great civilization in America, if we are to win leisure for cultivating the choice things of mind and spirit, we must put the machine in its place,” he wrote.

Deepfakes of your dead loved ones are a booming Chinese business

People are seeking help from AI-generated avatars to process their grief after a family member passes away.

• Zeyi Yang archive page

A wave of retractions is shaking physics

Grappling with problematic papers and poorly documented data, researchers and journal editors gathered in Pittsburgh to hash out the best way forward.

• Sophia Chen archive page

Why Threads is suddenly popular in Taiwan

During Taiwan’s presidential election, Meta’s social network emerged as a surprise hit.

Threads is giving Taiwanese users a safe space to talk about politics

But Meta's discomfort with political content could end up pushing them away.

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Research: Technology is changing how companies do business

I n the fast-paced world of modern business, technology plays a crucial role in shaping how companies operate. One area where this impact is particularly significant is in the organization of production chains—specifically the way goods are made and distributed.

A new study from the Cornell SC Johnson College of Business advances understanding of the U.S. production chain evolution amidst technological progress in information technology (IT), shedding light on the complex connections between business IT investments and organizational design.

Advances in IT have sparked significant changes in how companies design their production processes. In the paper "Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing," which published April 30 in Management Science , Chris Forman, the Peter and Stephanie Nolan Professor in the Dyson School of Applied Economics and Management, and his co-author delved into what these changes mean for businesses and consumers.

In running a manufacturing plant, a key decision is how much of the production process is handled in-house and how much is outsourced to other companies. This decision, known as vertical integration, can have big implications for a business. Advances in information and communication technology, such as those brought about by the internet, shifted the network of production flows for many firms.

Forman and Kristina McElheran, assistant professor of strategic management at University of Toronto, analyzed U.S. Census Bureau data of over 5,600 manufacturing plants to see how the production chains of businesses were affected by the internet revolution. Their use of census data allowed them to look inside the relationships among production units within and between companies and how transaction flows changed after companies invested in internet-enabled technology that facilitated coordination between them.

The production units of many of the companies in their study concurrently sold to internal and external customers, a mix they refer to as plural selling. They found that the reduction in communication costs enabled by the internet shifted the mix toward more sales outside of the firm, or less vertical integration.

"The internet has made it cheaper and faster for companies to communicate and share information with each other. This means they can work together more efficiently without the need for as much vertical integration," said Forman.

While some might worry that relying on external partners could make businesses more vulnerable, the research suggests otherwise. In fact, companies that were already using a plural governance approach before the internet age seem to be the most adaptable to these changes. Production units that were capacity-constrained were also among those that made the most significant changes to transaction flows after new technology investments.

"Technology is continuing to reshape the way companies operate and are organized," Forman said. "More recently, changes in the use of analytics in companies have been accompanied by changes in organizations, and the same is very likely ongoing with newer investments in artificial intelligence."

The research highlights the importance of staying ahead of the curve in technology. Companies that embrace digital technologies now are likely to be the ones that thrive in the future. And while there are still many unanswered questions about how these changes will play out, one thing is clear: The relationship between technology and business is only going to become more and more intertwined in the future.

More information: Chris Forman et al, Production Chain Organization in the Digital Age: Information Technology Use and Vertical Integration in U.S. Manufacturing, Management Science (2024). DOI: 10.1287/mnsc.2019.01586

Provided by Cornell University

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• Open access
• Published: 13 May 2024

Single-site iron-anchored amyloid hydrogels as catalytic platforms for alcohol detoxification

• Jiaqi Su   ORCID: orcid.org/0000-0003-2010-8388 1 , 2   na1 ,
• Pengjie Wang   ORCID: orcid.org/0000-0001-9975-6737 3   na1 ,
• Wei Zhou 4 ,
• Mohammad Peydayesh   ORCID: orcid.org/0000-0002-6265-3811 1 ,
• Jiangtao Zhou   ORCID: orcid.org/0000-0003-4248-2207 1 ,
• Tonghui Jin 1 ,
• Felix Donat   ORCID: orcid.org/0000-0002-3940-9183 5 ,
• Cuiyuan Jin 6 ,
• Lu Xia   ORCID: orcid.org/0000-0002-2726-5389 7 ,
• Kaiwen Wang   ORCID: orcid.org/0000-0002-1046-4525 7 ,
• Fazheng Ren   ORCID: orcid.org/0000-0001-6250-0754 3 ,
• Paul Van der Meeren   ORCID: orcid.org/0000-0001-5405-4256 2 ,
• F. Pelayo García de Arquer   ORCID: orcid.org/0000-0003-2422-6234 7 &
• Raffaele Mezzenga   ORCID: orcid.org/0000-0002-5739-2610 1 , 8  

Nature Nanotechnology ( 2024 ) Cite this article

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• Nanostructures

Constructing effective antidotes to reduce global health impacts induced by alcohol prevalence is a challenging topic. Despite the positive effects observed with intravenous applications of natural enzyme complexes, their insufficient activities and complicated usage often result in the accumulation of toxic acetaldehyde, which raises important clinical concerns, highlighting the pressing need for stable oral strategies. Here we present an effective solution for alcohol detoxification by employing a biomimetic-nanozyme amyloid hydrogel as an orally administered catalytic platform. We exploit amyloid fibrils derived from β-lactoglobulin, a readily accessible milk protein that is rich in coordinable nitrogen atoms, as a nanocarrier to stabilize atomically dispersed iron (ferrous-dominated). By emulating the coordination structure of the horseradish peroxidase enzyme, the single-site iron nanozyme demonstrates the capability to selectively catalyse alcohol oxidation into acetic acid, as opposed to the more toxic acetaldehyde. Administering the gelatinous nanozyme to mice suffering from alcohol intoxication significantly reduced their blood-alcohol levels (decreased by 55.8% 300 min post-alcohol intake) without causing additional acetaldehyde build-up. Our hydrogel further demonstrates a protective effect on the liver, while simultaneously mitigating intestinal damage and dysbiosis associated with chronic alcohol consumption, introducing a promising strategy in effective alcohol detoxification.

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Although widely enjoyed for its social and relaxing effects (Supplementary Fig. 1 ), alcohol consumption consistently poses significant risks to public health. In fact, in 2016 alone, harmful alcohol consumption resulted in nearly three million deaths and 132.6 million disability-adjusted life years 1 , 2 , 3 , 4 . Existing therapies, mainly relying on endogenous enzymes 5 , 6 , 7 , offer only temporary relief from symptoms, such as nausea and headaches, but fail to address other underlying issues, such as drowsiness, exhaustion and chronic alcoholism. Nanocomplexes with multiple complementary hepatic enzymes have emerged as an effective approach for accelerating human alcohol metabolism 8 , 9 . Although promising, a significant obstacle arises from the insufficient activity of commercially available enzymes, leading to the accumulation of a more hazardous intermediate, acetaldehyde, and possibly damage to human organs. Furthermore, natural enzymes possess major disadvantages, such as high cost, poor physicochemical stability and challenging storage, which have so far impeded the practical application of these complexes for alcohol detoxification purposes.

Over the past decades, advances in nanotechnology have facilitated the evolution of artificial enzymes into nanomaterials, that is, nanozymes, which have ignited enormous scientific interest across diverse fields, ranging from in vitro biosensing and detection to in vivo therapeutics 10 , 11 , 12 , 13 . Inspired by natural enzyme frameworks, researchers have predominantly focused on atomically distributed metal catalysts, in which the catalytic centre of natural enzymes is replicated at the atomic level 14 , 15 , 16 . These single-site catalysts, designed with well-defined electronic and geometric architectures, possess excellent catalytic capabilities, holding great potential as viable substitutes for natural enzymes. Given these promising prospects, attempts have been made to develop biomimetic nanozymes for alcohol detoxification by using, for example, natural enzymes on exogenous supports such as graphene oxide quantum dots or metal-organic framework nanozymes 17 , 18 . However, these approaches still either rely on natural enzymes or offer indirect effects, underscoring the potential for substantial design enhancements. The critical, yet challenging, aspect is the design of efficient single-site catalysts that are capable of converting ethanol into less-toxic acetic acid, or further into carbon dioxide and water, while minimizing the generation of acetaldehyde. Additionally, the task also lies in developing an orally administerable nanozyme that can withstand the gastrointestinal environment and which features no additional toxicity.

In this article, we report a biomimetic-nanozyme amyloid hydrogel to alleviate the deleterious effects of alcohol consumption via oral administration. Within this platform, single-site iron-anchored amyloid fibrils, an original kind of atomic-level engineered nanozyme featuring a similar coordination structure to horseradish peroxidase and with remarkable peroxidase-like activity, are used to efficiently catalyse alcohol oxidation. Specifically, the resultant nanozyme exhibits excellent selectivity in favour of acetic acid production. The catalytic activity of the gelatinous nanozyme could largely tolerate the digestive process, leading to a substantial decrease in blood alcohol levels in alcoholic mice, while avoiding the additional build-up of acetaldehyde. We finally demonstrate that this hydrogel also achieves heightened liver protection and substantial alleviation of intestinal damage and dysbiosis, thereby underscoring its potential as an improved therapeutic approach for alcohol-related conditions. By employing atomic-level design and harnessing the capabilities of nanozymes, our study offers promising insights into the development of efficient and targeted alcohol antidotes, with potential benefits for both liver protection and gastrointestinal health.

Synthesis of single-site iron-anchored β-lactoglobulin fibrils

Diverging from conventional methods that use inorganic carriers, in the current work, we sought to utilize a readily available protein material, β-lactoglobulin (BLG) amyloid fibrils, as the supportive framework for atomically dispersed iron. In addition to their intrinsic binding affinity to various metal ions 19 , including iron, the large aspect ratio of protein filaments (Supplementary Fig. 2a ) and tacked-up β-sheet units also enhance the accessibility of potential binding sites, thereby facilitating the high-density loading of iron atoms. Moreover, BLG fibrils can be easily derived from native BLG, a readily available milk protein, and have very recently been demonstrated safe nutrition ingredients by a comprehensive in vitro and in vivo assessment 20 , meeting the requirements for potential oral administration 21 . Moreover, the exceptional gelling property of BLG fibrils allows for the easy production of hydrogels 22 , which anticipates a delayed digestion process and a prolonged action time within the gastrointestinal tract due to their high viscoelasticity 23 , 24 .

The single-site iron-anchored BLG fibrils (Fe SA @FibBLG) catalyst was synthesized by a straightforward wetness impregnation procedure (Fig. 1a ), which involved exposing a dispersion of BLG fibrils in a mixture of ethanol and polyethylene glycol 200 (PEG200) to a Fe(NO 3 ) 3 PEG200 solution. During this process, the natural occurrence of nitrogen in BLG fibrils coordinated with iron ions to form functional Fe–N–C active sites. The resulting precipitate was lyophilized and collected after multiple rounds of centrifugation and washing.

a , Illustration of the synthesis process of Fe SA @FibBLG. b – d , TEM image ( b ), HAADF-STEM image ( c ) and the corresponding EDS mapping images ( d ) of Fe SA @FibBLG. e – g , AFM images of Fe SA @FibBLG ( e, f (I) ) and FibBLG ( f (II) ) on the mica surface and ( g ) the corresponding height profiles of the white auxiliary lines. h , Representative HAADF-STEM image of Fe SA @FibBLG. The images presented in b – f , h are representative of six technical replicates ( n  = 6), each yielding similar results.

Source data

Having synthesized Fe SA @FibBLG, we then performed a comprehensive characterization of the material using multiple analytical techniques. The morphology of Fe SA @FibBLG, which retains a nanometre-scale diameter consistent with pure BLG fibrils (Supplementary Fig. 2b ), suggests minimal structural impact from the integration of iron (Fig. 1b and Supplementary Fig. 2b ). The iron was homogeneously dispersed across the BLG fibril framework, as evidenced by a significant overlap of the Fe K-edge profile with the elemental composition of the BLG fibrils (Fig. 1c,d and Supplementary Fig. 2c ). Atomic force microscopy (AFM) images confirmed a consistent height of approximately 3 nm both before and after iron integration, verifying the negligible presence of crystalline iron or oxide species (Fig. 1e,f,g ). As shown in Fig. 1h and Supplementary Fig. 2d–f , the presence of individual bright dots with a size below 0.2 nm clearly demonstrated the atomic dispersion of single iron atoms over Fe SA FibBLG, indicating that iron, upon participating in the synthetic procedure described above, is present exclusively in single-site form on the BLG fibrils.

Structural analysis of Fe SA @FibBLG

The coordination environment of iron within Fe SA @FibBLG was elucidated by X-ray absorption fine structure (XAFS) spectroscopy 25 . Figure 2a shows that the pre-edge position for Fe SA @FibBLG resided between the positions of iron foil (metallic iron) and Fe 2 O 3 . The white line area located at higher binding energy demonstrates a lower oxidation state and different coordination environments compared with Fe 2 O 3 (ref. 26 ). X-ray absorption near-edge spectroscopy (XANES) features are valuable for discerning site symmetry around iron in macromolecular complexes 27 . A distinct prominent pre-edge feature below 7,120 eV indicates the ferrous iron (Fe 2+ ) square-planar coordination in iron(II) phthalocyanine (FePc), whereas in Fe SA @FibBLG this feature is slightly reduced due to deviations from ideal square-planarity 28 . The XANES spectrum of Fe SA @FibBLG (Fig. 2a , inset) closely resembles that of FePc, implying a positively charged ionic state of iron within Fe SA @FibBLG (Fe δ + , where the average δ is close to 2). Further insights were obtained from extended X-ray absorption fine structure (EXAFS) spectra in R -space (Fig. 2b ), which revealed a single peak at approximately 1.4 Å. From comparison with reference materials this peak was attributable to the backscattering between iron and lighter atoms, primarily nitrogen (Fig. 2b ), supporting the atomic dispersion of iron sites within Fe SA @FibBLG. Wavelet transform analysis differentiated the sample from the iron foil reference by showing a single maximum intensity at approximately 4 Å −1 and 1.4 Å, suggesting significant Fe–N contributions (Fig. 2c and Supplementary Fig. 3 ), with the coordination number of iron estimated to be 4.5 (Fig. 2d and Supplementary Table 1 ). However, given the challenge in distinguishing Fe–N from Fe–O coordination compared to references such as FePc and Fe 2 O 3 , it is crucial to emphasize the potential existence of Fe–O bonds. Collectively, these findings confirmed that iron in Fe SA @FibBLG exists as single-site iron, devoid of any crystalline or oxide iron metal structure and mainly coordinates with nitrogen atoms. X-ray photoelectron spectroscopy (XPS) analysis of Fe SA @FibBLG further identified distinct binding states of carbon, nitrogen, oxygen and iron, demonstrating a majority of single-site iron in the Fe 2+ state and the existence of Fe–N coordination (Supplementary Figs. 4 and 5 ) 29 , 30 , 31 .

a , Normalized XANES spectra at the Fe K-edge of Fe SA @FibBLG along with reference samples. b , Fourier-transformed (FT) magnitudes of the experimental Fe K-edge EXAFS signals of Fe SA @FibBLG along with reference samples. c , Wavelet transform analysis of Fe K-edge EXAFS data. d , Fitting curves of the EXAFS of FeSA@FibBLG in the R -space and k -space (inset). Fitting results are summarized in Supplementary Table 1 . e , Representative snapshots of the assembly structure of 102 amyloid-forming fragments (LACQCL) from BLG in the process of AAMD simulation using the Gromacs54A force field at 10 ns. f , The 3D gradient isosurfaces and corresponding 2D scatter diagram of δg versus sign( λ 2 )ρ for possible non-covalent interactions between a single iron atom and dimer intercepted from BLG fibril segments in e through DFT simulation. δg is a quantitative measure derived from comparing electron density gradients in the presence and absence of interference, highlighting the penetration of electron density from one Bader atom to its neighbor; sign(λ 2 ) ρ is a scalar field value used to describe the product of the sign of the second eigenvalue (λ 2 ) of the Hessian matrix of a scalar field and the scalar field’s density ( ρ ).

Next, we performed a density functional theory (DFT) calculation for the process of anchoring a ferric ion onto the BLG fibril structure. Since the formation of BLG fibrils involved the participation of multiple peptides assembling in a random manner, here a model nanofibre structure was generated in silico based on repetitive amyloid-forming fragments (LACQCL) from BLG, using an all-atom molecular dynamics (AAMD) simulation (Fig. 2e ) 19 . An evident periodic nanofibril was formed at 10 ns containing 102 repetitive fragments, where a peptide dimer with verified thermodynamic stability was intercepted for DFT calculation (Supplementary Fig. 6 ). As shown in Fig. 2f , the blue isosurface observed between the iron atom and surrounding nitrogen atoms corresponds to strong attractive interactions between iron and nitrogen, potentially arising from the sharing of electron pairs between the iron and nitrogen atoms (Supplementary Fig. 7 ). This was further verified by the existence of the prominent peak at approximately −0.03 in the scatter plot (Fig. 2f ). These results clearly demonstrate that the BLG fibrils possessed effective binding sites that were capable of capturing iron atoms through Fe–N coordination, enabling the formation of active iron centres in Fe SA @FibBLG.

Peroxidase-like activity of Fe SA @FibBLG

The coordination structure of the catalytic sites in our Fe SA @FibBLG was similar to that of the horseradish peroxidase enzyme (Supplementary Fig. 8a ) 32 . Inspired by this similarity, we characterized the peroxidase-like activities of Fe SA @FibBLG by studying the facilitated chromogenic reactions through catalysing artificial substrates of peroxidase (for example, 3,3′,5,5′-tetramethylbenzidine (TMB), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) or o -phenylenediamine) in the presence of H 2 O 2 (Supplementary Fig. 8b ). By using the general method described in the current work, two comparison catalysts, namely, single-site iron-anchored BLG (Fe SA @BLG), and iron-nanoparticle-anchored BLG fibrils (FeNP@FibBLG), were synthesized and then used to characterize the enzymatic activity (Supplementary Figs. 9 and 10 and Supplementary Table 2 ). Using TMB as a substrate, the specific activity (SA) values (U mg −1 ) of these nanozymes were measured: the SA of Fe SA @FibBLG was markedly superior, at 95.0 U mg −1 , approximately 1.7 and 10.1 times higher than the SAs of Fe SA @BLG (57.3 U mg −1 ) and FeNP@FibBLG (9.38 U mg −1 ), respectively (Fig. 3a ). Steady-state kinetic assays revealed that Fe SA @FibBLG exhibited superior catalytic performance among the tested nanozymes in oxidizing TMB, with remarkable kinetic parameters including maximum reaction rate ( V max  = 0.788 μM s −1 ), turnover number ( K cat  = 21.9 min −1 ), catalytic efficiency ( K cat / K m  = 5.47 × 10 8  M −1  min −1 ) and selectivity ( K m  = 4.00 × 10 –2  mM) (Fig. 3b and Supplementary Table 3 ). We also determined the kinetic parameters for the H 2 O 2 substrate, which further substantiated the exceptional catalytic performance of Fe SA @FibBLG (Supplementary Table 4 ).

a – f , Typical Michaelis–Menten curves of Fe SA @FibBLG, Fe SA @BLG and FeNP@FibBLG by varying the TMB ( a ), ethanol ( c ) and acetaldehyde ( e ) concentrations in the presence of H 2 O 2 . Comparison of the SAs (U mg −1 ) of Fe SA @FibBLG, Fe SA @BLG and FeNP@FibBLG on TMB ( b ), ethanol ( d ) and acetaldehyde ( f ) oxidation in the presence of H 2 O 2 . One nanozyme activity unit (U) is defined as the amount of nanozyme that catalyses 1 µmol of product per minute. The SAs (U mg −1 ) were determined by plotting the nanozyme activities against their weight and measuring the gradients of the fitting curves. 1 H NMR spectrum of the reaction products of Fe SA @FibBLG-catalysed ethanol (inset d ) and acetaldehyde (inset f ) oxidation. Data are presented as the mean ± s.d. from n  = 3 independent experiments. g , EPR spectra of 5,5-dimethyl-pyrroline- N -oxide/H 2 O 2 solution upon the addition of nanozymes. h , Schematic illustration of the peroxidase-like activities of Fe SA @FibBLG when exposed to various substrates.

Interestingly, Fe SA @FibBLG also exhibited a notable capacity for catalytically oxidizing ethanol and acetaldehyde in the presence of H 2 O 2 (Fig. 3c–f ). The SA of Fe SA @FibBLG achieved a value of 7.90 U mg −1 when ethanol was used as the substrate, remarkably surpassing the other two reference catalysts. The superior catalytic efficacy of Fe SA @FibBLG with respect to ethanol was further confirmed by determining its kinetic parameters, which indicate it achieves a catalytic efficiency ( K cat / K m  = 4.11 × 10 5  M −1  min −1 ) that exceeds that of Fe SA @BLG ( K cat / K m  = 8.66 × 10 4  M −1  min −1 ) by 4.7 times and FeNP@FibBLG ( K cat / K m  = 9.25 × 10 3  M −1  min −1 ) by 44.4 times (Supplementary Table 5 ). Fe SA @FibBLG also manifested the lowest K m value when ethanol was the substrate, signifying its excellent affinity towards ethanol. It is important to note that Fe SA @FibBLG could directly oxidize ethanol to acetic acid, yielding formic acid as the only by-product, without generating any detectable acetaldehyde intermediate, as evidenced by 1 H NMR (Fig. 3d , inset).

To explain this, we performed a steady-state kinetic analysis of Fe SA @FibBLG participating in acetaldehyde oxidation. We found Fe SA @FibBLG to have the lowest K m value of the evaluated nanozymes, signifying its superior substrate affinity towards acetaldehyde. The K cat / K m for this reaction (3.89 × 10 5  M −1  min −1 ) was very close to that for ethanol oxidation (4.11 × 10 5  M −1  min −1 ) (Supplementary Tables 5 and 6 ). Upon the reaction between these nanozymes and H 2 O 2 , the electron paramagnetic resonance (EPR) spectrum exhibited characteristic peaks associated with 5,5-dimethyl-pyrroline- N -oxide–OH · , with Fe SA @FibBLG displaying the strongest EPR signal, indicating the highest production of OH · (Fig. 3g ). The same characteristic peaks were observed in the EPR spectrum of the Fe SA @FibBLG/H 2 O 2 /ethanol reaction system (Supplementary Fig. 17 ), confirming the existence of OH · in ethanol oxidation—a finding that agrees with numerous studies demonstrating the efficacy of OH · in oxidizing diverse organic compounds, including ethanol and acetaldehyde 33 , 34 . Nevertheless, it is essential to emphasize that our investigation serves as a preliminary exploration of the free radicals involved in this reaction; a more comprehensive mechanistic investigation is required for an in-depth understanding of the catalytic process.

Additionally, the catalytic stability of Fe SA @FibBLG was assessed by high-resolution transmission electron microscopy (TEM), high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), energy-dispersive spectroscopy elemental analysis, X-ray diffraction and XPS (Supplementary Figs. 18 and 19 ). Fe SA @FibBLG did not exhibit substantial morphological or oxidation state alterations and effectively preserved the high atomic dispersion of iron active sites throughout the catalysis. It is also worth mentioning that Fe SA @FibBLG retained at least 95.2% and 84.1% of its activity after undergoing 3 h of digestion in simulated gastric and intestinal fluids, respectively (Supplementary Fig. 20 ). The robust stability observed in Fe SA @FibBLG may be due to the reduction effects of BLG fibril support 21 .

Protective potential on acute alcohol intoxication

Even a single new onset of blood alcohol that exceeds the detoxifying capability of the hepatic system can induce individual symptoms of acute alcohol intoxication, such as hepatocyte destruction, stress response and cognitive deficits 35 , 36 . To mitigate potential damage to the human digestive tract from direct H 2 O 2 ingestion, a biomimetic cascade catalysis system was designed by integrating gold nanoparticles (AuNPs) for onsite and sustainable H 2 O 2 generation 37 , 38 , 39 . AuNPs have demonstrated exceptionally efficient and enduring catalytic activity similar to glucose oxidase, which allows the conversion of glucose into gluconic acid, accompanied by the production of adequate H 2 O 2 (Supplementary Fig. 21 ). Because protein fibrils transiently remained and were mostly digested (generally within 4 h) in the gastrointestinal tract 20 , where the majority of alcohol was absorbed, a salt-induced technique 40 ( Methods ) was followed to fabricate the AuNP-attached Fe SA @FibBLG amyloid hydrogel (Fe SA @AH) (Supplementary Fig. 22 ) to achieve prolonged retention within the gastrointestinal tract, and, thereby, an enhanced overall capacity for ethanol oxidation. The resultant Fe SA @AH showed typical self-standing ability, obvious nanofibril structures (exceptional birefringence under polarized light) and good syringability (Fig. 4a ). We then labelled Fe SA @AH with [ 18 F]fluoro-2-deoxyglucose ([ 18 F]FDG) and visualized its transportation in C57BL/6 mice by using micro positron emission tomography (PET)–computed tomography (CT) scanning. The metabolism of Fe SA @AH took more than 6 h in the upper gastrointestinal tract after gavage, which indicated an extended retention time in vivo due to the hydrogel nature of the compound 20 .

a , Visualization (1) and microstructures (2) of Fe SA @AH under polarized light, and injectability test (3). b , Time-series images of gastrointestinal translocation of [ 18 F]FDG-loaded Fe SA @AH in mice (0–6 h). c , Schematic of acute alcohol intoxication model construction ( Methods ). Created with BioRender.com. d , Effect of different treatment (PBS, AH and Fe SA @AH) on alcohol tolerance time and sobering-up time in C57BL/6 mice. e , Representative trajectory of search strategies of mice with different treatments. f , g , Escape latencies ( f ) and path length ( g ) of four groups of mice. h , i , Mean concentrations of blood alcohol ( h ) and acetaldehyde ( i ) in alcohol-intoxicated mice treated with PBS, AH and Fe SA @AH. j , Serum levels of ALT and AST enzyme levels in four groups of mice. Data are obtained for n  = 8 independent biological replicates, mean ± s.e.m. P values in d , f , g , h , j were tested by one-way analysis of variance followed by Tukey–Kramer test. * P  < 0.05, ** P  < 0.01, *** P  < 0.001, **** P  < 0.0001.

The prophylactic benefits of Fe SA @AH administration were assessed in an alcohol-treated murine model 41 (Fig. 4c ). A group of ethanol-free, but PBS-gavaged mice served as a negative control; all the ethanol-gavaged mice were asleep for alcohol intoxication. Although they tolerated alcohol intake for a longer period of time ( ∼ 40 min), the Fe SA @AH mice were awoken significantly earlier ( ∼ 2 h) than other intoxicated groups (Fig. 4d ). We then conducted the Morris water maze (MWM) test 6 h post-alcohol intake to quantitatively assess murine spatial reference memory (Fig. 4e ). Grouped mean swimming speeds of alcohol-exposed mice were comparable to those of the blank group, indicating recovery of fundamental activities (Supplementary Fig. 25a ). However, PBS- and AH-treated mice showed increased search time and distance to locate the hidden platform, whereas the mice given Fe SA @AH demonstrated markedly improved navigational efficiency (Fig. 4f,g ). Additionally, distinct search strategies were observed, with PBS and AH groups favouring less efficient patterns, in contrast to the strategic approaches of the Fe SA @AH and control groups (Supplementary Fig. 25b ).

Aetiologically, behavioural abnormalities were attributed to alcohol and its in vivo intermediate metabolite, acetaldehyde 42 , and the liver played a core role in ethanolic metabolism. Prophylactic Fe SA @AH immediately and persistently reduced the mice blood alcohol (BA) concentration by a significant amount (Fig. 4h ). The BA in Fe SA @AH mice decreased by 41.3%, 40.4%, 42.0%, 46.6% and 55.8%, respectively, 30, 60, 120, 180 and 300 min post-gavaging. Importantly, the above-mentioned process induced no additional acetaldehyde (BAce) accumulation in blood (Fig. 4i ), which plays a crucial role in safeguarding the liver, as the build-up of acetaldehyde is known to be a catalyst for liver cirrhosis and hepatocellular carcinoma. Stress responses of liver were definitely mitigated, which was revealed by the significantly decreased blood alanine aminotransferase (ALT), aspartate aminotransferase (AST), malondialdehyde (MDA) and glutathione (GSH) levels in the Fe SA @AH group (Fig. 4j and Supplementary Fig. 26a,b ).

Prophylactic effect on chronic alcohol intoxication

The NIAAA model (mouse model of chronic and binge ethanol feeding) was conducted to confirm the long-term beneficial effects of Fe SA @AH 43 . After model constructions (Fig. 5a and Methods ), the PBS mice showed a significantly decreased body weight, increased liver injury (ballooning degeneration and multifocal inflammatory cell infiltration) and hepatic lipid accumulation compared with the blank (Fig. 5b,c ). Notably, Fe SA @AH-rescued mice showed a significantly decreased loss in body weight, less liver damage and re-regulated hepatic lipid metabolism (Fig. 5b,c ) from intoxication. Moreover, mice treated with Fe SA @AH had lower BA than those with PBS and AH (Supplementary Fig. 27a ). It is worth noting, however, that Fe SA @AH also decreased the BAce concentration (Supplementary Fig. 27b ), indicating its dominant competitive role in ethanol elimination to endogenous ADH. Significant lower blood ALT and AST levels further confirmed the inflammation alleviation effect of Fe SA @AH on the liver (Fig. 5d ). Additionally, administration of Fe SA @AH also significantly suppressed triglyceride and total cholesterol accumulation in ethanol-fed mice (Supplementary Fig. 28e–j ).

a , Schematic of the chronic alcohol intoxication model construction ( Methods ). Created with BioRender.com. b , Body weight changes in the four groups of mice during the feeding period. c , Representative H&E-stained images of liver in the four groups. d , Serum ALT and AST levels in mice. e , H&E images of colon (left part) and its assessed scores (right histogram) in different groups of mice. f , Immunofluorescence staining of the tight junction proteins in the colon (left part, 30× magnification). The tight junction proteins (Claudin-1, occludin and ZO-1) were stained green whereas the 4,6-diamidino-2-phenylindole (DAPI) was blue. The histograms (right) show the mean density of the normalized levels of occludin and ZO-1. IOD, integrated optical density. g , Taxonomic and phylogenetic tree of the top 21 most affected genera (genus with >10% mean abundance change in at least one group compared to others) by different treatments generated by GraPhlAn 4.0. Outer circles show the grouped mean relative abundance of each genus. h , Metabolic processes of alcohol to acetate and further in mice. The left colour blocks indicate the endogenous organs, liver, intestine and gut microbiota involved in alcohol decomposition, and the right shows the path in which Fe SA @AH participated. The box-plot shows the relative levels of ko00770 pantothenate and CoA biosynthesis among groups (minimum–maximum). The heatmap shows 83 significantly changed pathways compared with those in the PBS group. Source data are provided as a Source Data file. i , LPS concentrations of mice in the four groups. Data are shown in the form of mean ± s.e.m. from n  = 8 biological replicates. In c , e , f , the images displayed are representative of three independent biological replicates ( n  = 3), each producing consistent results. For histopathological, physiological and biochemical indexes ( c – f , i ), P values were tested by one-way analysis of variance followed by Tukey–Kramer test whereas the pairwise Wilcoxon test with Bonferroni–Holm correction was used for microbial taxa ( h , i ). * P  < 0.05, ** P  < 0.01, *** P  < 0.001.

The gut and its symbionts (the microbiota) are important, but usually overlooked, alcohol-metabolizing organs 44 , 45 , 46 . Chronic alcohol consumption caused histopathological changes in the colon, destroyed epithelial cells, atrophied goblet cells and resulted in inflammatory cell infiltration (Fig. 5e ), and also weakened permeability (Fig. 5f ), which may cause more microbial components to enter the bloodstream 47 . Alcohol also induced significant compositional shifts (β-diversity) in the gut microbiota of mice (Supplementary Fig. 29a ), but showed limited effects on the Shannon index and percentage of Gram-negative bacteria (Supplementary Fig. 29b,c ). Consistently 48 , the mean abundance of Bacteroidota increased in all alcohol-treated groups. Another dominant phylum, Firmicutes , decreased significantly in the PBS group compared with the blank group (Supplementary Fig. 29d ). Interestingly, a significant loss of functional murine-mucoprotein-degrading bacteria, Akkermansia ( verrucomicrobiota ), and transitions of Ileibacterium and Allobaculum (blank) to Bacteroides and Prevotellaceae_UCG-001 (PBS), were identified (Fig. 5g ).

In terms of functional profiles, we found no significant intergroup gut microbial function changes due to ethanol-related processes (Supplementary Table 10 ). In accordance with previous research 47 , gut microbiota were determined to be indirectly involved in ethanol metabolism, especially acetate-induced microbial anaerobic respiration, such as the glycolysis/gluconeogenesis (ko00010) and pentose phosphate pathway (ko00030) (Supplementary Table 10 ). Alcohol consumption also induced significantly overexpressed pantothenate. Moreover, CoA biosynthesis (ko00770) and the citrate cycle (TCA) (ko00020) constituted important carbon unit donors for further processes (Fig. 5h ), such as lipopolysaccharide (LPS) biosynthesis (ko00540)—LPS is widely recognized as an endotoxin that can induce hepatic inflammation 49 . This epithelial pathophysiological damage and intraluminal dysbiosis were significantly mitigated by Fe SA @AH compared with other AHs (Fig. 5e–h ). Furthermore, as one of the final beneficial outputs, the concentration of blood LPS was significantly decreased in Fe SA @AH-treated mice (Fig. 5i ).

In aggregate, we have demonstrated the design of a single-site iron-anchored amyloid hydrogel with remarkable catalytic oxidation capacity for alcohol as a highly efficient catalytic platform for in vivo alcohol metabolism. This work provides compelling evidence for the viability of a biomimetic-nanozyme-based hydrogel as an orally applied antidote for alcohol intoxication. Fe SA @AH demonstrates exceptional preference for acetic acid production, enabling a rapid decrease in blood alcohol levels while simultaneously mitigating the risk of excessive acetaldehyde accumulation, and markedly surpasses the effectiveness of existing alcohol intoxication antidotes that rely on a combination of natural enzymes. Unlike the predominantly liver-centric human intrinsic alcohol metabolism, orally administered Fe SA @AH directs this process towards the gastrointestinal tract, providing increased safety for the liver. In addition, despite this shift in the site of alcohol metabolism, there is no manifestation of additional adverse gastrointestinal symptoms; in fact, Fe SA @AH shows a remarkable alleviation of intestinal damage and dysbiosis induced by alcohol consumption, further demonstrating its potential for clinical translation.

The findings from our study outline a general and efficient strategy for synthesizing a diverse group of orally applied biomimetic nanozymes, and establish the foundation for future investigations aimed at maximizing the potential of artificial enzyme design in different therapeutic applications.

Synthesis of catalysts

BLG (>98%) was purchased from Davisco Foods International and purified using a previously reported protocol 50 . For a detailed description of BLG fibril preparation, see ref. 51 . For the synthesis of Fe SA @FibBLG, 100 mg lyophilized BLG fibril powder was dispersed in a mixture of 8.0 ml ethanol and 1.9 ml PEG200. The dispersion was then subjected to argon bubbling for 30 min to remove the dissolved oxygen, followed by irradiation under a xenon lamp with an ultraviolet filter (250–380 nm, 27.9 mW cm −2 , PLS-SXE300CUV) for 10 min to generate free radicals. Subsequently, 0.1 ml of 108.21 mg ml −1 Fe(NO 3 ) 3 ·9H 2 O EDTA solution was added dropwise to the dispersion of BLG fibrils under magnetic stirring for 12 h at 25 °C. Fe SA @BLG was prepared by the same synthesis procedure as for Fe SA @FibBLG, except that the BLG fibril powder was replaced by an equal amount of BLG powder. For the synthesis of FeNP@FibBLG, the as-obtained Fe SA @FibBLG dispersion was further ultraviolet-irradiated for 18 min under anaerobic conditions to reduce the iron ions. Finally, samples were collected by centrifugation at 4 °C, 11,100 g for 10 min, washed by ethanol (10.0 ml × 6) and resuspended in 5.0 ml deionized water (pH 2). The powdered Fe SA @FibBLG, Fe SA @BLG and FeNP@FibBLG were obtained by lyophilization and stored at 4 °C.

Characterizations

The high-resolution TEM images and elemental mappings were recorded with an FEI Talos F200X microscope at accelerating voltages of 80 kV and 200 kV, respectively. AFM images were obtained using a Bruker Multimode 8 scanning probe microscope. HAADF-STEM images were captured using an FEI Titan Themis G2 microscope equipped with a probe spherical aberration corrector and operated at 300 keV. The crystalline structure and phase purity were detected by a powder diffractometer (Siemens D500 with Cu Kα radiation (λ = 1.5406 Å)). The iron loadings on catalysts were analysed by inductively coupled plasma mass spectrometry (Elan DRC-e, Perkin Elmer). The X-ray absorption structure spectra (Fe K-edge) were collected at beamline BL44B2 of the SPring-8 synchrotron (Japan), operated at 8.0 GeV with a maximum current of 250 mA. Data were collected in transmission mode using a Si(111) double-crystal monochromator. The EXAFS data were analysed using the ATHENA module implemented in IFEFFIT software (CARS). XPS measurements were performed using a multipurpose spectrometer (Sigma Probe, Thermo VG Scientific) with a monochromatic Al Kα X-ray source. EPR spectra were acquired using a Bruker X-band (9.4 GHz) EMXplus 10/12 spectrometer equipped with an Oxford Instruments ESR-910 liquid helium cryostat. All spectra were collected under ambient conditions. Solution 1 H NMR spectra were collected on a Bruker DRX 300 spectrometer (7.05 T; Larmor frequency, 300 MHz ( 1 H)) in deuterated water (D 2 O) at room temperature.

MD simulations

All of the AAMD simulations were performed on a GROMACS 2018 package using a gromacs54A force field 52 . The box size of the initial model was 12 × 12 × 30 nm 3 , including an SPC/E water model and 102 peptide chains (sequence, LACQCL) 19 under three-dimensional periodic boundary conditions. A spherical cut-off of 1.0 nm was used for the summation of van der Waals interactions and short-range Coulomb interactions, and the particle-mesh Ewald method 53 . The temperature and pressure of the system were controlled by means of a velocity rescaling thermal thermostat and a Berendsen barostat. At first, the energy of the system was minimized in small steps to balance the initial velocity of the molecules. Then, the NPT ensemble using a leapfrog integrator with a time step of 1.0 fs was used to simulate the system for 8 ns at 300 K, which is sufficient for the balance of the system. Dynamic snapshot images were generated in Visual Molecular Dynamics 1.9.3 54 .

DFT calculations

To investigate the interaction between iron ions and the system, one iron ion was inserted into the peptide dimer, and the structure was optimized by DFT using the CP2K software package 55 . The Perdew–Burke-Ernzerhof generalized gradient approximation functional was adopted to describe the electronic exchange and correlation, in conjunction with the DZVP-MOLOPT-SR-GTH basis set for all atoms (C, H, O, N, Fe). The structure was optimized with the spin multiplicity to treat the doublet spin state and the charge of the iron ion was set to +2 e . The convergence criterion for the absolute value of the maximum force was set to 4.5 × 10 −4  a.u. and the r.m.s. of all forces to 3 × 10 −4  a.u. Grimme’s DFT-D3 method was adopted for correcting van der Waals interactions 56 .The interaction of the system was characterized by the independent gradient model method, and the based isosurface maps were rendered by Visual Molecular Dynamics from the cube files exported from Multiwfn 3.8 (ref. 57 ).

Peroxidase-like activity

The peroxidase-like activities of nanozymes were assessed at 37 °C using 350 μl of HAc–NaAc buffer (0.1 M, pH 4.0) with varied nanozyme concentrations, using TMB as the substrate. Following the addition of 20 μl of TMB solution (20 mM in dimethylsulfoxide) and 20 μl of H 2 O 2 solution (2 M), 10 μl of nanozymes with varying concentrations was introduced into the system. The catalytic oxidation of TMB (oxTMB) was quantified by measuring the absorbance at 652 nm via an ultraviolet–visible spectrometer. The steady-state kinetics analysis was executed by modifying the concentrations of TMB and H 2 O 2 . To derive the Michaelis–Menten constant, we performed Lineweaver–Burk plot analysis using the double reciprocal of the Michaelis–Menten equation, ν  =  ν max  × [ S ]/( K m  + [S]), where ν denotes the initial velocity, ν max represents the maximum reaction velocity, [ S ] indicates the substrate concentration and K m is the Michaelis constant. Additionally, the catalytic rate constant ( k cat ) was computed as k cat = ν max /[ E ], where [ E ] signifies the molar concentration of metal within the nanozymes. By employing diverse pH buffer solutions, we explored the pH dependency of the peroxidase-like activity of nanozymes, spanning a range from pH 2 to 9. Similarly, we investigated its temperature sensitivity by observing its activity at various temperatures, progressively increasing from 20 °C to 60 °C.

Catalytic oxidation activity on alcohol and acetaldehyde

The catalytic oxidation activities of nanozymes on both alcohol and acetaldehyde were carried out at 37 °C in 350 μl of HAc–NaAc buffer (0.1 M, pH 4.0), with varying nanozyme concentrations (10 μl). Subsequent to adding 20 μl of H 2 O 2 solution (2 M), 20 μl of ethanol or acetaldehyde solution (2 mM) was introduced into separate tubes containing the reaction mixture. Quantification of the catalytic oxidation of ethanol or acetaldehyde was performed using the Ethanol Assay Kit (ab65343) and Acetaldehyde Assay Kit (ab308327) from Abcam Biotechnology. Through altering the concentrations of ethanol or acetaldehyde, steady-state kinetics analysis was carried out, and the Michaelis–Menten constant was determined by analysing Lineweaver–Burk plots involving the double reciprocal of the Michaelis–Menten equation. Additionally, the identification of the reaction products was confirmed by 1 H NMR spectrometry.

Catalytic activity assessment of nanozymes during in vitro simulation of the digestion process

We adhered to the INFOGEST standard protocol for nanozyme digestion to replicate the physiological human gastrointestinal digestion process 58 . In this methodology, stock solutions of simulated gastric fluid and simulated intestinal fluid were prepared and equilibrated at 37 °C prior to use. For gastric digestion, 2 ml of the nanozyme (1 mg ml −1 ) was mixed with 2 ml of simulated gastric fluid stock solution, and porcine pepsin solution was added to achieve a final enzyme activity of 500 U per mg of protein. CaCl 2 (H 2 O) 2 was then introduced into the mixture to reach a final concentration of 0.15 mM prior to adjusting the pH to 3 using 5 M HCl. The mixture was transferred to a water bath shaker (VWR 462-0493) at 37 °C and sampled at 30 and 60 min, after which NaOH solution was used to deactivate the enzyme. Following the gastric digestion, pancreatin (0.1 mg ml −1 ) was dissolved in simulated intestinal fluid containing 0.6 mM CaCl 2 and added to the gastric digests in a 1:1 (v/v) ratio to initiate intestinal digestion, which lasted for 120 min at 37 °C with regular sampling every 30 min. The samples were freeze-dried immediately after collection for enzyme activity evaluation experiments using TMB as a substrate, in which the amount of nanozyme after digestion was normalized.

Hydrogel formation

Gelation of Fe SA @FibBLG dispersion containing AuNPs (Fe SA @AH) was achieved following our previously reported procedure with some modifications 40 . For the synthesis of AuNPs, all glassware was cleaned with freshly prepared aqua regia (HCl:HNO 3  = 3:1 vol/vol) and then thoroughly rinsed with water. A 2 ml solution of BLG fibrils (2.0 wt%, pH 2.0) was mixed with a 40 mM HAuCl 4 solution to reach a final protein:gold mass ratio of 14.7:1. The mixture underwent a chemical reduction through the dropwise addition of a NaBH 4 solution (0.8 ml) under a nitrogen atmosphere. The resulting solution was then dialysed to remove any remaining NaBH 4 and concentrated to 2 ml with a dialysis membrane (Spectra/Por, molecular weight cut-off, 6–8 kDa, Spectrum Laboratories) against a 6 wt% PEG solution ( M r  ≈ 35,000, Sigma-Aldrich) at pH 2.0. TEM imaging of AuNPs stabilized by BLG fibrils revealed three-dimensional particles with an average size of 1.32 nm (Supplementary Fig. 21a ), determined by analysing six TEM images using ImageJ software v.1.8.0. For the preparation of Fe SA @AH, 2 g of Fe SA @FibBLG powder was dissolved in the resulting AuNP-attached BLG fibril solution (2 ml). The mixture was then transferred into a plastic syringe, the top part of which had been previously cut. The plastic syringe was covered with a section of a dialysis tube (Spectra/Por, molecular weight cut-off, 6–8 kDa), and the head of the syringe was positioned in direct contact with an excess of 300 mM NaCl solution at pH 7.4 for at least 16 h in a 4 °C cold room to facilitate gelation. The resulting hydrogel sample was kept under 4 °C. The working hydrogel was freshly prepared by mixing the aforementioned hydrogel with 0.1 ml of a glucose solution (8.0 M) immediately before further characterization or detoxification use. A BLG fibril hydrogel was obtained using the same procedure, except that the Fe SA @FibBLG was replaced with an equal amount of BLG fibril dispersion.

Murine models

Male wild-type C57BL/6 mice, 20–25 g and 8–10 weeks old, were purchased from Beijing Vital River Laboratory Animal Technology. All of the murine experiments in the current study were approved by the Regulations of Beijing Laboratory Animal Management (approval number AW40803202-5-1) and conducted according to the guidelines set forth in the Institutional Animal Care and Use Committee of China Agricultural University.

Acute model

Thirty-two male C57BL/6 mice were randomly divided into four groups after 12 h fasting. Mice were orally gavaged with AH and Fe SA @AH (at doses of 10 ml per kg (body weight)), and two groups of mice received the same volume of PBS (as controls, the blank and the PBS groups), respectively. After 20 min of adaptation, mice from the AH, Fe SA @AH, and PBS groups were orally administered an alcohol liquid diet (10 g per kg (body weight)), while the same volume of PBS was administered for the blank group. All the mice were killed 6 h later.

Chronic model

A mouse model of chronic and binge ethanol feeding (NIAAA model) was conducted following the protocol proposed by Bertola et al. 43 . In brief, after 5 days of ad libitum Lieber–DeCarli diet adaptation, 32 mice were randomly divided into four groups: (1) a control group (Con) of mice were pair-fed with the control diet; (2) an ethanol diet group (EtOH); (3) an ethanol diet group with additional 10 ml per kg (body weight) AH; and (4) an ethanol diet group with additional 10 ml per kg (body weight) Fe SA @AH. The ethanol-fed groups were granted unrestricted access to the ethanol Lieber–DeCarli diet containing 5% (vol/vol) ethanol for 10 days, and additionally received daily morning (9:00) gavage of PBS, AH or Fe SA @AH, respectively. The control group was pair-fed with an isocaloric control diet and daily control-liquid gavage. All animals were maintained in specific pathogen-free conditions, at a temperature of 23 ± 1 °C and 50–60% humidity, under a 12 h light/dark cycle, with access to autoclaved water. On day 16, both the ethanol-fed and pair-fed mice were orally administered a single dose of ethanol (5 g per kg (body weight)) or isocaloric maltose dextrin at 9:20, respectively, and killed 6 h later. The body weight of mice was recorded every 2 days.

After overnight fasting, mice were gavaged with 0.1 ml [ 18 F]FDG-labelled Fe SA @AH. Then, mice were anaesthetized with oxygen containing 2% isoflurane, and placed in and fixed in a prone position in an imaging chamber. Time-series images were obtained with an Inveon microPET/CT scanner (Siemens); the scanner parameters were a 15 min CT scan (80 kVp, 500 μA, 1,100 ms exposure time) followed by a 10 min PET acquisition. Quantification of images was performed by AMIDE software 3.0.

Alcohol tolerance test

Approximately 10 µl of blood was collected from the submandibular vein at 30, 60, 90, 120, 180 and 300 min after alcohol exposure. In the chronic model, sampling was conducted after the binge ethanol feeding. Blood alcohol concentration (BAC) was determined using a test kit from Abcam Biotechnology (ab65343). BACs were normalized to mice body weights as previously described 8 . Normalized BAC, BAC nor , was calculated using the equation: BAC nor  = BAC i  × (BWT i /BWT ave ), where BAC i and BWT i denote the blood alcohol level and body weight of mice, respectively, and BWT ave represents the average weight of all mice in each set of experiments. The quantification of the BAce concentration was carried out using a test kit obtained from Abcam Biotechnology (ab308327), and the normalization process was conducted using the same method as for the BAC.

Alcohol tolerance time was the duration between alcohol administration and the absence of righting reflex, while the duration of the absence of righting reflex was recorded as the sobering-up time. Mice that became ataxic were considered to have lost their righting reflex, and were then placed face up. The time point at which the mice returned to their normal upright position signified they had regained their righting reflex.

An MWM test 59 was conducted by Anhui Zhenghua Biologic Apparatus Facilities, as described previously. Specifically, the MWM apparatus comprised a large circular pool (120 cm diameter and 40 cm height) which was filled with TiO 2 -dyed, 25 °C thermostatic water, and a 10-cm-diameter platform was positioned and fixed 2 cm below the water surface. Before acute ethanol exposure, mice received four rounds of daily training for 6 days. Each trial was limited to 60 s, and the time that it took for the mice to successfully locate the platform was recorded. On day 7, mice were retested (no platform condition) 5 h after ethanol feeding (the time point by which all mice regained their consciousness and mobility). The tested items included trajectory, path length, escape latency and swimming speed (MWM animal behaviour video tracking system, Morris v.2.0).

Biochemical assays

Blood samples were collected through cardiac puncture from anaesthetized mice 6 h after alcohol gavage. Prior to testing, samples were maintained at ambient temperature for 4 h, and then centrifuged (864.9 g , 4 °C) for 20 min. Supernatants were suctioned and stored at −80 °C for further analysis. Serum ALT, AST, triglycerides and total cholesterol were measured by a Hitachi Biochemistry Analyzer 7120 (Hitachi High-Tech).

Weighed liver tissues were collected and immersed immediately in 10% neutral buffered formaldehyde. After overnight fixation, tissues were embedded in paraffin and cut into 5 μm sections for further haematoxylin and eosin (H&E) and oil red O (Sigma) staining. Images were captured by a Nikon Eclipse TI-SR fluorescence microscope. Fresh liver was homogenized in chilled normal saline and centrifuged (1,500 g , 4 °C) for 15 min. GSH and MDA levels of the resultant supernatant were detected using the GSH assay kit (ab65322) and the lipid peroxidation (MDA) assay kit (ab118970), respectively. Hepatic and cellular lipid content was isolated using the chloroform/methanol-based method 60 , and quantified by using the triglyceride assay kit (ab65336) and the mouse total cholesterol ELISA kit (ab285242, SSUF-C), respectively.

Colon histology and immunohistochemistry

Colon length, caecum to anus, was measured, and the distal colon was washed with saline, with one-half being fixed with 4% paraformaldehyde, and the other half stored at −80 °C. Histological measurements of the colon were the same as those for the liver.

For immunofluorescence, colon tissues were treated with EDTA buffer and boiled to expose the antigens. Tissues were then incubated overnight at 4 °C with primary antibody and washed three times for 5 min each with PBS. Subsequently, colon tissues were covered with secondary antibody and incubated at room temperature in the dark for 50 min, followed by another set of three 5 min washes with PBS. The resultant sections were mounted with a mounting medium and stained with 4,6-diamidino-2-phenylindole. Slides were then covered, and the images were captured using a Nikon Eclipse Ti inverted fluorescence microscope.

Microbiota changes

Faecal samples were collected within 5 min after defecation into a sterile tube and stored at −80 °C. Microbial genome DNA was extracted from faeces by using the DNeasy PowerSoil Pro Kit (QIAGEN) according to the manufacturer’s instructions, and the variable 3–4 (V4-v4) region of the 16S rRNA gene was PCR-amplified using barcoded 338F-806R primers (forward primer, 5′-ACTCCTACGGGAGGCAGCAG-3′; reverse primer, 5′-GGACTACHVGGGTWTCTAAT-3′). PCR components contained 25 μl of Phusion High-Fidelity PCR Master Mix, 3 μl (10 μM) of each forward and reverse primer, 10 μl of the DNA template, 3 μl of DMSO and 6 μl of double-distilled H2O. The following cycling conditions were used: initial denaturation at 98 °C for 30 s, followed by 25 cycles of denaturation at 98 °C for 15 s, annealing at 58 °C for 15 s, and extension at 72 °C for 15 s, and a final extension of 1 min at 72 °C. PCR amplicons were purified using Agencourt AMPure XP Beads (Beckman Coulter) and quantified using a PicoGreen dsDNA Assay Kit (Invitrogen). After quantification, amplicons were pooled in equal amounts, and 2 × 150 bp paired-end sequencing was performed using the Illumina Miseq PE300 platform at GUHE Info Technology. Amplicon sequence variants (ASVs) were denoised and clustered by the UNOISE algorithm. Taxa bar plots, and α- and β-diversity analysis, were performed with QIIME 2 v.2020.6 and the R package v.3.6.3. Metabolic function was predicted using PICRUSt2, and the output file was further analysed using the STAMP software package (v.2.1.3).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All the data that validates the outcomes of this study are included in the Article and its Supplementary Information files. For any other relevant source data, interested parties can obtain them from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

Simulation files and code used for modelling iron-anchored BLG fibril segments can be accessed via Zenodo at: https://doi.org/10.5281/zenodo.10819612 (ref. 61 ).

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Acknowledgements

The authors thank I. Kutzli for the purification of BLG, W. Wang for 1 H NMR measurements, and M. Wörle for X-ray diffraction experiments. Bruna F. G. L. is gratefully acknowledged for the help in analysing XAFS data. Appreciation is also extended to C. Zeder for the inductively coupled plasma mass spectrometry measurements. Support from R. Schäublin during electron microscopy observations is gratefully acknowledged. J.S. acknowledges financial support from the Special Research Fund of Ghent University (BOF.PDO.2021.0050.01) and the Research Foundation–Flanders (FWO V420422N). ICFO authors thank CEX2019-000910-S (MCIN/AEI/10.13039/501100011033), Fundació Cellex, Fundació Mir-Puig, Generalitat de Catalunya through CERCA and the La Caixa Foundation (100010434, EU Horizon 2020 Marie Skłodowska-Curie grant agreement 847648).

Open access funding provided by Swiss Federal Institute of Technology Zurich.

Author information

These authors contributed equally: Jiaqi Su, Pengjie Wang.

Authors and Affiliations

Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland

Jiaqi Su, Mohammad Peydayesh, Jiangtao Zhou, Tonghui Jin & Raffaele Mezzenga

Particle and Interfacial Technology Group, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium

Jiaqi Su & Paul Van der Meeren

Department of Nutrition and Health, Beijing Higher Institution Engineering Research Center of Animal Products, China Agricultural University, Beijing, China

Pengjie Wang & Fazheng Ren

Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland

Institute of Energy and Process Engineering, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

Felix Donat

Institute of Translational Medicine, Zhejiang Shuren University, Zhejiang, China

Cuiyuan Jin

ICFO–Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, Barcelona, Spain

Lu Xia, Kaiwen Wang & F. Pelayo García de Arquer

Department of Materials, ETH Zurich, Zurich, Switzerland

Raffaele Mezzenga

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Contributions

R.M. and J.S. conceived the idea, designed the experiments, co-wrote the manuscript and coordinated the overall research project. J.S. developed the fabrication procedure of protein-fibril-based single-atom nanozymes, characterized the enzymatic activities of nanozymes, collected and analysed the data, and performed the computational analysis. L.X. and K.W. performed the XAFS measurements of samples and analysed the data. T.J and M.P. assisted in the analysis of enzyme kinetics data. W.Z. performed XPS and 1 H NMR measurements of samples and analysed the data. J.Z. coordinated the AFM characterization of samples. P.W. and F.R. designed the in vitro and in vivo experiments on cells and animals. P.W. and J.S. carried out cell and animal studies. C.J. contributed to the microbiota test and data analysis. P.V.d.M., F.P.G.d.A. and F.D. contributed to interpreting the data and revised the manuscript. All the authors discussed the results and commented on the manuscript.

Corresponding authors

Correspondence to Jiaqi Su or Raffaele Mezzenga .

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J.S. and R.M. are the inventors of a patent filed jointly by Ghent University and ETH Zurich (EP24153321).

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Nature Nanotechnology thanks Marco Frasconi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

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Supplementary Methods, Discussion, Figs. 1–29, Tables 1–10, Gating strategy for flow cytometry and References.

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Su, J., Wang, P., Zhou, W. et al. Single-site iron-anchored amyloid hydrogels as catalytic platforms for alcohol detoxification. Nat. Nanotechnol. (2024). https://doi.org/10.1038/s41565-024-01657-7

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Received : 10 October 2023

Accepted : 21 March 2024

Published : 13 May 2024

DOI : https://doi.org/10.1038/s41565-024-01657-7

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