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• NATURE INDEX
• 09 December 2020

Six researchers who are shaping the future of artificial intelligence

• Gemma Conroy ,
• Hepeng Jia ,
• Benjamin Plackett &

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Andy Tay is a science writer in Singapore.

As artificial intelligence (AI) becomes ubiquitous in fields such as medicine, education and security, there are significant ethical and technical challenges to overcome.

CYNTHIA BREAZEAL: Personal touch

Credit: Taj Francis

While the credits to Star Wars drew to a close in a 1970s cinema, 10-year-old Cynthia Breazeal remained fixated on C-3PO, the anxious robot. “Typically, when you saw robots in science fiction, they were mindless, but in Star Wars they had rich personalities and could form friendships,” says Breazeal, associate director of the Massachusetts Institute of Technology (MIT) Media Lab in Cambridge, Massachusetts. “I assumed these robots would never exist in my lifetime.”

A pioneer of social robotics and human–robot interaction, Breazeal has made a career of conceptualizing and building robots with personality. As a master’s student at MIT’s Humanoid Robotics Group, she created her first robot, an insectile machine named Hannibal that was designed for autonomous planetary exploration and funded by NASA.

Some of the best-known robots Breazeal developed as a young researcher include Kismet, one of the first robots that could demonstrate social and emotional interactions with humans; Cog, a humanoid robot that could track faces and grasp objects; and Leonardo, described by the Institute of Electrical and Electronics Engineers in New Jersey as “one of the most sophisticated social robots ever built”.

Nature Index 2020 Artificial intelligence

In 2014, Breazeal founded Jibo, a Boston-based company that launched her first consumer product, a household robot companion, also called Jibo. The company raised more than US$70 million and sold more than 6,000 units. In May 2020, NTT Disruption, a subsidiary of London-based telecommunications company, NTT, bought the Jibo technology, and plans to explore the robot’s applications in health care and education.

Breazeal returned to academia full time this year as director of the MIT Personal Robots Group. She is investigating whether robots such as Jibo can help to improve students’ mental health and wellbeing by providing companionship. In a preprint published in July, which has yet to be peer-reviewed, Breazeal’s team reports that daily interactions with Jibo significantly improved the mood of university students ( S. Jeong et al . Preprint at https://arxiv.org/abs/2009.03829; 2020 ). “It’s about finding ways to use robots to help support people,” she says.

In April 2020, Breazeal launched AI Education, a free online resource that teaches children how to design and use AI responsibly. “Our hope is to turn the hundreds of students we’ve started with into tens of thousands in a couple of years,” says Breazeal. — by Benjamin Plackett

CHEN HAO: Big picture

Analysing medical images is an intensive and technical task, and there is a shortage of pathologists and radiologists to meet demands. In a 2018 survey by the UK’s Royal College of Pathologists, just 3% of the National Health Service histopathology departments (which study diseases in tissues) said they had enough staff. A June 2020 report published by the Association of American Medical Colleges found that the United States’ shortage of physician specialists could climb to nearly 42,000 by 2033.

AI systems that can automate part of the process of medical imaging analysis could be the key to easing the burden on specialists. They can reduce tasks that usually take hours or days to seconds, says Chen Hao, founder of Imsight, an AI medical imaging start-up based in Shenzhen, China.

Launched in 2017, Imsight’s products include Lung-Sight, which can automatically detect and locate signs of disease in CT scans, and Breast-Sight, which identifies and measures the metastatic area in a tissue sample. “The analysis allows doctors to make a quick decision based on all of the information available,” says Chen.

Since the outbreak of COVID-19, two of Shenzhen’s largest hospitals have been using Imsight’s imaging technology to analyse subtle changes in patients’ lungs caused by treatment, which enables doctors to identify cases with severe side effects.

In 2019, Chen received the Young Scientist Impact Award from the Medical Image Computing and Computer-Assisted Intervention Society, a non-profit organization in Rochester, Minnesota. The award recognized a paper he led that proposed using a neural network to process fetal ultrasound images ( H. Chen et al. in Medical Image Computing and Computer-Assisted Intervention — MICCAI 2015 (eds N. Navab et al. ) 507–514; Springer, 2015 ). The technique, which has since been adopted in clinical practice in China, reduces the workload of the sonographer.

Despite the rapid advancement of AI’s role in health care, Chen rejects the idea that doctors can be easily replaced. “AI will not replace doctors,” he says. “But doctors who are better able to utilize AI will replace doctors who cannot.” — by Hepeng Jia

ANNA SCAIFE: Star sifting

When construction of the Square Kilometre Array (SKA) is complete , it will be the world’s largest radio telescope. With roughly 200 radio dishes in South Africa and 130,000 antennas in Australia expected to be installed by the 2030s, it will produce an enormous amount of raw data, more than current systems can efficiently transmit and process.

Anna Scaife, professor of radio astronomy at the University of Manchester, UK, is building an AI system to automate radio astronomy data processing. Her aim is to reduce manual identification, classification and cataloguing of signals from astronomical objects such as radio galaxies, active galaxies that emit more light at radio wavelengths than at visible wavelengths.

In 2019, Scaife was the recipient of the Jackson-Gwilt Medal, one of the highest honours bestowed by the UK Royal Astronomical Society (RAS). The RAS recognized a study led by Scaife, which outlined data calibration models for Europe’s Low Frequency Array (LOFAR) telescope, the largest radio telescope operating at the lowest frequencies that can be observed from Earth ( A. M. M. Scaife and G. H. Heald Mon. Not. R. Astron. Soc. 423 , L30–L34; 2012 ). The techniques in Scaife’s paper underpin most low-frequency radio observations today.

“It’s a very peculiar feeling to win an RAS medal,” says Scaife. “It’s a mixture of excitement and disbelief, especially because you don’t even know that you were being considered, so you don’t have any opportunity to prepare yourself. Suddenly, your name is on a list that commemorates more than 100 years of astronomy history, and you’ve just got to deal with that.”

Scaife is the academic co-director of Policy@Manchester, the University of Manchester’s policy engagement institute, where she helps researchers to better communicate their findings to policymakers. She also runs a data science training network that involves South African and UK partner universities, with the aim to build a team of researchers to work with the SKA once it comes online. “I hope that the training programmes I have developed can equip young people with skills for the data science sector,” says Scaife. — by Andy Tay

TIMNIT GEBRU: Algorithmic bias

Computer vision is one of the most rapidly developing areas of AI. Algorithms trained to read and interpret images are the foundation of technologies such as self-driving cars, surveillance and augmented reality.

Timnit Gebru, a computer scientist and former co-lead of the Ethical AI Team at Google in Mountain View, California, recognizes the promise of such advances, but is concerned about how they could affect underrepresented communities, particularly people of colour . “My research is about trying to minimize and mitigate the negative impacts of AI,” she says.

In a 2018 study , Gebru and Joy Buolamwini, a computer scientist at the MIT Media Lab, concluded that three commonly used facial analysis algorithms drew overwhelmingly on data obtained from light-skinned people ( J. Buolamwini and T. Gebru. Proc. Mach. Learn. Res. 81 , 77–91; 2018 ). Error rates for dark-skinned females were found to be as high as 34.7% , due to a lack of data, whereas the maximum error rate for light-skinned males was 0.8%. This could result in people with darker skin getting inaccurate medical diagnoses, says Gebru. “If you’re using this technology to detect melanoma from skin photos, for example, then a lot of dark-skinned people could be misdiagnosed.”

Facial recognition used for government surveillance, such as during the Hong Kong protests in 2019, is also highly problematic , says Gebru, because the technology is more likely to misidentify a person with darker skin. “I’m working to have face surveillance banned,” she says. “Even if dark-skinned people were accurately identified, it’s the most marginalized groups that are most subject to surveillance.”

In 2017, as a PhD student at Stanford University in California under the supervision of Li Fei-Fei , Gebru co-founded the non-profit, Black in AI, with Rediet Abebe, a computer scientist at Cornell University in Ithaca, New York. The organization seeks to increase the presence of Black people in AI research by providing mentorship for researchers as they apply to graduate programmes, navigate graduate school, and enter and progress through the postgraduate job market. The organization is also advocating for structural changes within institutions to address bias in hiring and promotion decisions. Its annual workshop calls for papers with at least one Black researcher as the main author or co-author. — by Benjamin Plackett

YUTAKA MATSUO: Internet miner

In 2010, Yutaka Matsuo created an algorithm that could detect the first signs of earthquakes by monitoring Twitter for mentions of tremors. His system not only detected 96% of the earthquakes that were registered by the Japan Meteorological Agency (JMA), it also sent e-mail alerts to registered users much faster than announcements could be broadcast by the JMA.

He applied a similar web-mining technique to the stock market. “We were able to classify news articles about companies as either positive or negative,” says Matsuo. “We combined that data to accurately predict profit growth and performance.”

Matsuo’s ability to extract valuable information from what people are saying online has contributed to his reputation as one of Japan’s leading AI researchers. He is a professor at the University of Tokyo’s Department of Technology Management and president of the Japan Deep Learning Association, a non-profit organization that fosters AI researchers and engineers by offering training and certification exams. In 2019, he was the first AI specialist added to the board of Japanese technology giant Softbank.

Over the past decade, Matsuo and his team have been supporting young entrepreneurs in launching internationally successful AI start-ups. “We want to create an ecosystem like Silicon Valley, which Japan just doesn’t have,” he says.

Among the start-ups supported by Matsuo is Neural Pocket, launched in 2018 by Roi Shigematsu, a University of Tokyo graduate. The company analyses photos and videos to provide insights into consumer behaviour.

Matsuo is also an adviser for ReadyFor, one of Japan’s earliest crowd-funding platforms. The company was launched in 2011 by Haruka Mera, who first collaborated with Matsuo as an undergraduate student at Keio University in Tokyo. The platform is raising funds for people affected by the COVID-19 pandemic, and reports that its total transaction value for donations rose by 4,400% between March and April 2020.

Matsuo encourages young researchers who are interested in launching AI start-ups to seek partnerships with industry. “Japanese society is quite conservative,” he says. “If you’re older, you’re more likely to get a large budget from public funds, but I’m 45, and that’s still considered too young.” — by Benjamin Plackett

DACHENG TAO: Machine visionary

By 2030, an estimated one in ten cars globally will be self-driving. The key to getting these autonomous vehicles on the road is designing computer-vision systems that can identify obstacles to avoid accidents at least as effectively as a human driver .

Neural networks, sets of AI algorithms inspired by neurological processes that fire in the human cerebral cortex, form the ‘brains’ of self-driving cars. Dacheng Tao, a computer scientist at the University of Sydney, Australia, designs neural networks for computer-vision tasks. He is also building models and algorithms that can process videos captured by moving cameras, such as those in self-driving cars.

“Neural networks are very useful for modelling the world,” says Tao, director of the UBTECH Sydney Artificial Intelligence Centre, a partnership between the University of Sydney and global robotics company UBTECH.

In 2017, Tao was awarded an Australian Laureate Fellowship for a five-year project that uses deep-learning techniques to improve moving-camera computer vision in autonomous machines and vehicles. A subset of machine learning, deep learning uses neural networks to build systems that can ‘learn’ through their own data processing.

Since launching in 2018, Tao’s project has resulted in more than 40 journal publications and conference papers. He is among the most prolific researchers in AI research output from 2015 to 2019, as tracked by the Dimensions database, and is one of Australia’s most highly cited computer scientists. Since 2015, Tao’s papers have amassed more than 42,500 citations, as indexed by Google Scholar. In November 2020, he won the Eureka Prize for Excellence in Data Science, awarded by the Australian Museum.

In 2019, Tao and his team trained a neural network to construct 3D environments using a motion-blurred image, such as would be captured by a moving car. Details, including the motion, blurring effect and depth at which it was taken, helped the researchers to recover what they describe as “the 3D world hidden under the blurs”. The findings could help self-driving cars to better process their surroundings. — by Gemma Conroy

Nature 588 , S114-S117 (2020)

doi: https://doi.org/10.1038/d41586-020-03411-0

This article is part of Nature Index 2020 Artificial intelligence , an editorially independent supplement. Advertisers have no influence over the content.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

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[ go to the annotated version ]

Until the turn of the millennium, AI’s appeal lay largely in its promise to deliver, but in the last fifteen years, much of that promise has been redeemed. [15]  AI already pervades our lives. And as it becomes a central force in society, the field is now shifting from simply building systems that are intelligent to building intelligent systems that are human-aware and trustworthy.

Several factors have fueled the AI revolution. Foremost among them is the maturing of machine learning, supported in part by cloud computing resources and wide-spread, web-based data gathering. Machine learning has been propelled dramatically forward by “deep learning,” a form of adaptive artificial neural networks trained using a method called backpropagation. [16]  This leap in the performance of information processing algorithms has been accompanied by significant progress in hardware technology for basic operations such as sensing, perception, and object recognition. New platforms and markets for data-driven products, and the economic incentives to find new products and markets, have also contributed to the advent of AI-driven technology.

All these trends drive the “hot” areas of research described below. This compilation is meant simply to reflect the areas that, by one metric or another, currently receive greater attention than others. They are not necessarily more important or valuable than other ones. Indeed, some of the currently “hot” areas were less popular in past years, and it is likely that other areas will similarly re-emerge in the future.

Large-scale machine learning

Many of the basic problems in machine learning (such as supervised and unsupervised learning) are well-understood. A major focus of current efforts is to scale existing algorithms to work with extremely large data sets. For example, whereas traditional methods could afford to make several passes over the data set, modern ones are designed to make only a single pass; in some cases, only sublinear methods (those that only look at a fraction of the data) can be admitted.

Deep learning

The ability to successfully train convolutional neural networks has most benefited the field of computer vision, with applications such as object recognition, video labeling, activity recognition, and several variants thereof. Deep learning is also making significant inroads into other areas of perception, such as audio, speech, and natural language processing.

Reinforcement learning

Whereas traditional machine learning has mostly focused on pattern mining, reinforcement learning shifts the focus to decision making, and is a technology that will help AI to advance more deeply into the realm of learning about and executing actions in the real world. It has existed for several decades as a framework for experience-driven sequential decision-making, but the methods have not found great success in practice, mainly owing to issues of representation and scaling. However, the advent of deep learning has provided reinforcement learning with a “shot in the arm.” The recent success of AlphaGo, a computer program developed by Google Deepmind that beat the human Go champion in a five-game match, was due in large part to reinforcement learning. AlphaGo was trained by initializing an automated agent with a human expert database, but was subsequently refined by playing a large number of games against itself and applying reinforcement learning.

Robotic navigation, at least in static environments, is largely solved. Current efforts consider how to train a robot to interact with the world around it in generalizable and predictable ways. A natural requirement that arises in interactive environments is manipulation, another topic of current interest. The deep learning revolution is only beginning to influence robotics, in large part because it is far more difficult to acquire the large labeled data sets that have driven other learning-based areas of AI. Reinforcement learning (see above), which obviates the requirement of labeled data, may help bridge this gap but requires systems to be able to safely explore a policy space without committing errors that harm the system itself or others. Advances in reliable machine perception, including computer vision, force, and tactile perception, much of which will be driven by machine learning, will continue to be key enablers to advancing the capabilities of robotics.

Computer vision

Computer vision is currently the most prominent form of machine perception. It has been the sub-area of AI most transformed by the rise of deep learning. Until just a few years ago, support vector machines were the method of choice for most visual classification tasks. But the confluence of large-scale computing, especially on GPUs, the availability of large datasets, especially via the internet, and refinements of neural network algorithms has led to dramatic improvements in performance on benchmark tasks (e.g., classification on ImageNet [17] ). For the first time, computers are able to perform some (narrowly defined) visual classification tasks better than people. Much current research is focused on automatic image and video captioning.

Natural Language Processing

Often coupled with automatic speech recognition, Natural Language Processing is another very active area of machine perception. It is quickly becoming a commodity for mainstream languages with large data sets. Google announced that 20% of current mobile queries are done by voice, [18]  and recent demonstrations have proven the possibility of real-time translation. Research is now shifting towards developing refined and capable systems that are able to interact with people through dialog, not just react to stylized requests.

Collaborative systems

Research on collaborative systems investigates models and algorithms to help develop autonomous systems that can work collaboratively with other systems and with humans. This research relies on developing formal models of collaboration, and studies the capabilities needed for systems to become effective partners. There is growing interest in applications that can utilize the complementary strengths of humans and machines—for humans to help AI systems to overcome their limitations, and for agents to augment human abilities and activities.

Crowdsourcing and human computation

Since human abilities are superior to automated methods for accomplishing many tasks, research on crowdsourcing and human computation investigates methods to augment computer systems by utilizing human intelligence to solve problems that computers alone cannot solve well. Introduced only about fifteen years ago, this research now has an established presence in AI. The best-known example of crowdsourcing is Wikipedia, a knowledge repository that is maintained and updated by netizens and that far exceeds traditionally-compiled information sources, such as encyclopedias and dictionaries, in scale and depth. Crowdsourcing focuses on devising innovative ways to harness human intelligence. Citizen science platforms energize volunteers to solve scientific problems, while paid crowdsourcing platforms such as Amazon Mechanical Turk provide automated access to human intelligence on demand. Work in this area has facilitated advances in other subfields of AI, including computer vision and NLP, by enabling large amounts of labeled training data and/or human interaction data to be collected in a short amount of time. Current research efforts explore ideal divisions of tasks between humans and machines based on their differing capabilities and costs.

Algorithmic game theory and computational social choice

New attention is being drawn to the economic and social computing dimensions of AI, including incentive structures. Distributed AI and multi-agent systems have been studied since the early 1980s, gained prominence starting in the late 1990s, and were accelerated by the internet. A natural requirement is that systems handle potentially misaligned incentives, including self-interested human participants or firms, as well as automated AI-based agents representing them. Topics receiving attention include computational mechanism design (an economic theory of incentive design, seeking incentive-compatible systems where inputs are truthfully reported), computational social choice (a theory for how to aggregate rank orders on alternatives), incentive aligned information elicitation (prediction markets, scoring rules, peer prediction) and algorithmic game theory (the equilibria of markets, network games, and parlor games such as Poker—a game where significant advances have been made in recent years through abstraction techniques and no-regret learning).

Internet of Things (IoT)

A growing body of research is devoted to the idea that a wide array of devices can be interconnected to collect and share their sensory information. Such devices can include appliances, vehicles, buildings, cameras, and other things. While it's a matter of technology and wireless networking to connect the devices, AI can process and use the resulting huge amounts of data for intelligent and useful purposes. Currently, these devices use a bewildering array of incompatible communication protocols. AI could help tame this Tower of Babel.

Neuromorphic Computing

Traditional computers implement the von Neumann model of computing, which separates the modules for input/output, instruction-processing, and memory. With the success of deep neural networks on a wide array of tasks, manufacturers are actively pursuing alternative models of computing—especially those that are inspired by what is known about biological neural networks—with the aim of improving the hardware efficiency and robustness of computing systems. At the moment, such “neuromorphic” computers have not yet clearly demonstrated big wins, and are just beginning to become commercially viable. But it is possible that they will become commonplace (even if only as additions to their von Neumann cousins) in the near future. Deep neural networks have already created a splash in the application landscape. A larger wave may hit when these networks can be trained and executed on dedicated neuromorphic hardware, as opposed to simulated on standard von Neumann architectures, as they are today.

[15]  Appendix I offers a short history of AI, including a description of some of the traditionally core areas of research, which have shifted over the past six decades.

[16]  Backpropogation is an abbreviation for "backward propagation of errors,” a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of a loss function with respect to all the weights in the network.

[17]  ImageNet, Stanford Vision Lab, Stanford University, Princeton University, 2016, accessed August 1, 2016,  www.image-net.org/ .

[18]  Greg Sterling, "Google says 20% of mobile queries are voice searches,"  Search Engine Land , May 18, 2016, accessed August 1, 2016,  http://searchengineland.com/google-reveals-20-percent-queries-voice-queries-249917 .

In this section

Overall Trends and the Future of AI Research

Cite This Report

Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, Kevin Leyton-Brown, David Parkes, William Press, AnnaLee Saxenian, Julie Shah, Milind Tambe, and Astro Teller.  "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University, Stanford, CA,  September 2016. Doc:  http://ai100.stanford.edu/2016-report . Accessed:  September 6, 2016.

Report Authors

AI100 Standing Committee and Study Panel 

© 2016 by Stanford University. Artificial Intelligence and Life in 2030 is made available under a Creative Commons Attribution-NoDerivatives 4.0 License (International):  https://creativecommons.org/licenses/by-nd/4.0/ .

Artificial Intelligence

Since the 1950s, scientists and engineers have designed computers to "think" by making decisions and finding patterns like humans do. In recent years, artificial intelligence has become increasingly powerful, propelling discovery across scientific fields and enabling researchers to delve into problems previously too complex to solve. Outside of science, artificial intelligence is built into devices all around us, and billions of people across the globe rely on it every day. Stories of artificial intelligence—from friendly humanoid robots to SkyNet—have been incorporated into some of the most iconic movies and books.

But where is the line between what AI can do and what is make-believe? How is that line blurring, and what is the future of artificial intelligence? At Caltech, scientists and scholars are working at the leading edge of AI research, expanding the boundaries of its capabilities and exploring its impacts on society. Discover what defines artificial intelligence, how it is developed and deployed, and what the field holds for the future.

Artificial Intelligence Terms to Know >

What Is AI ?

Artificial intelligence is transforming scientific research as well as everyday life, from communications to transportation to health care and more. Explore what defines AI, how it has evolved since the Turing Test, and the future of artificial intelligence.

What Is the Difference Between "Artificial Intelligence" and "Machine Learning"?

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What is Generative AI?

Generative AI applications such as ChatGPT, a chatbot that answers questions with detailed written responses; and DALL-E, which creates realistic images and art based on text prompts; became widely popular beginning in 2022 when companies released versions of their applications that members of the public, not just experts, could easily use.

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Terms to Know

A set of instructions or sequence of steps that tells a computer how to perform a task or calculation. In some AI applications, algorithms tell computers how to adapt and refine processes in response to data, without a human supplying new instructions.

Artificial intelligence describes an application or machine that mimics human intelligence.

A system in which machines execute repeated tasks based on a fixed set of human-supplied instructions.

A system in which a machine makes independent, real-time decisions based on human-supplied rules and goals.

The massive amounts of data that are coming in quickly and from a variety of sources, such as internet-connected devices, sensors, and social platforms. In some cases, using or learning from big data requires AI methods. Big data also can enhance the ability to create new AI applications.

An AI system that mimics human conversation. While some simple chatbots rely on pre-programmed text, more sophisticated systems, trained on large data sets, are able to convincingly replicate human interaction.

Deep Learning

A subset of machine learning . Deep learning uses machine learning algorithms but structures the algorithms in layers to create "artificial neural networks." These networks are modeled after the human brain and are most likely to provide the experience of interacting with a real human.

Human in the Loop

An approach that includes human feedback and oversight in machine learning systems. Including humans in the loop may improve accuracy and guard against bias and unintended outcomes of AI.

Model (computer model)

A computer-generated simplification of something that exists in the real world, such as climate change , disease spread, or earthquakes . Machine learning systems develop models by analyzing patterns in large data sets. Models can be used to simulate natural processes and make predictions.

Neural Networks

Interconnected sets of processing units, or nodes, modeled on the human brain, that are used in deep learning to identify patterns in data and, on the basis of those patterns, make predictions in response to new data. Neural networks are used in facial recognition systems, digital marketing, and other applications.

Singularity

A hypothetical scenario in which an AI system develops agency and grows beyond human ability to control it.

Training data

The data used to " teach " a machine learning system to recognize patterns and features. Typically, continual training results in more accurate machine learning systems. Likewise, biased or incomplete datasets can lead to imprecise or unintended outcomes.

Turing Test

An interview-based method proposed by computer pioneer Alan Turing to assess whether a machine can think.

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• Review article
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Systematic review of research on artificial intelligence applications in higher education – where are the educators?

• Olaf Zawacki-Richter   ORCID: orcid.org/0000-0003-1482-8303 1 ,
• Victoria I. Marín   ORCID: orcid.org/0000-0002-4673-6190 1 ,
• Melissa Bond   ORCID: orcid.org/0000-0002-8267-031X 1 &
• Franziska Gouverneur 1  

International Journal of Educational Technology in Higher Education volume  16 , Article number:  39 ( 2019 ) Cite this article

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According to various international reports, Artificial Intelligence in Education (AIEd) is one of the currently emerging fields in educational technology. Whilst it has been around for about 30 years, it is still unclear for educators how to make pedagogical advantage of it on a broader scale, and how it can actually impact meaningfully on teaching and learning in higher education. This paper seeks to provide an overview of research on AI applications in higher education through a systematic review. Out of 2656 initially identified publications for the period between 2007 and 2018, 146 articles were included for final synthesis, according to explicit inclusion and exclusion criteria. The descriptive results show that most of the disciplines involved in AIEd papers come from Computer Science and STEM, and that quantitative methods were the most frequently used in empirical studies. The synthesis of results presents four areas of AIEd applications in academic support services, and institutional and administrative services: 1. profiling and prediction, 2. assessment and evaluation, 3. adaptive systems and personalisation, and 4. intelligent tutoring systems. The conclusions reflect on the almost lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education.

Introduction

Artificial intelligence (AI) applications in education are on the rise and have received a lot of attention in the last couple of years. AI and adaptive learning technologies are prominently featured as important developments in educational technology in the 2018 Horizon report (Educause, 2018 ), with a time to adoption of 2 or 3 years. According to the report, experts anticipate AI in education to grow by 43% in the period 2018–2022, although the Horizon Report 2019 Higher Education Edition (Educause, 2019 ) predicts that AI applications related to teaching and learning are projected to grow even more significantly than this. Contact North, a major Canadian non-profit online learning society, concludes that “there is little doubt that the [AI] technology is inexorably linked to the future of higher education” (Contact North, 2018 , p. 5). With heavy investments by private companies such as Google, which acquired European AI start-up Deep Mind for $400 million, and also non-profit public-private partnerships such as the German Research Centre for Artificial Intelligence Footnote 1 (DFKI), it is very likely that this wave of interest will soon have a significant impact on higher education institutions (Popenici & Kerr, 2017 ). The Technical University of Eindhoven in the Netherlands, for example, recently announced that they will launch an Artificial Intelligence Systems Institute with 50 new professorships for education and research in AI. Footnote 2

The application of AI in education (AIEd) has been the subject of research for about 30 years. The International AIEd Society (IAIED) was launched in 1997, and publishes the International Journal of AI in Education (IJAIED), with the 20th annual AIEd conference being organised this year. However, on a broader scale, educators have just started to explore the potential pedagogical opportunities that AI applications afford for supporting learners during the student life cycle.

Despite the enormous opportunities that AI might afford to support teaching and learning, new ethical implications and risks come in with the development of AI applications in higher education. For example, in times of budget cuts, it might be tempting for administrators to replace teaching by profitable automated AI solutions. Faculty members, teaching assistants, student counsellors, and administrative staff may fear that intelligent tutors, expert systems and chat bots will take their jobs. AI has the potential to advance the capabilities of learning analytics, but on the other hand, such systems require huge amounts of data, including confidential information about students and faculty, which raises serious issues of privacy and data protection. Some institutions have recently been established, such as the Institute for Ethical AI in Education Footnote 3 in the UK, to produce a framework for ethical governance for AI in education, and the Analysis & Policy Observatory published a discussion paper in April 2019 to develop an AI ethics framework for Australia. Footnote 4

Russel and Norvig ( 2010 ) remind us in their leading textbook on artificial intelligence, “All AI researchers should be concerned with the ethical implications of their work” (p. 1020). Thus, we would like to explore what kind of fresh ethical implications and risks are reflected by the authors in the field of AI enhanced education. The aim of this article is to provide an overview for educators of research on AI applications in higher education. Given the dynamic development in recent years, and the growing interest of educators in this field, a review of the literature on AI in higher education is warranted.

Specifically, this paper addresses the following research questions in three areas, by means of a systematic review (see Gough, Oliver, & Thomas, 2017 ; Petticrew & Roberts, 2006 ):

How have publications on AI in higher education developed over time, in which journals are they published, and where are they coming from in terms of geographical distribution and the author’s disciplinary affiliations?

How is AI in education conceptualised and what kind of ethical implications, challenges and risks are considered?

What is the nature and scope of AI applications in the context of higher education?

The field AI originates from computer science and engineering, but it is strongly influenced by other disciplines such as philosophy, cognitive science, neuroscience, and economics. Given the interdisciplinary nature of the field, there is little agreement among AI researchers on a common definition and understanding of AI – and intelligence in general (see Tegmark, 2018 ). With regard to the introduction of AI-based tools and services in higher education, Hinojo-Lucena, Aznar-Díaz, Cáceres-Reche, and Romero-Rodríguez ( 2019 ) note that “this technology [AI] is already being introduced in the field of higher education, although many teachers are unaware of its scope and, above all, of what it consists of” (p. 1). For the purpose of our analysis of artificial intelligence in higher education, it is desirable to clarify terminology. Thus, in the next section, we explore definitions of AI in education, and the elements and methods that AI applications might entail in higher education, before we proceed with the systematic review of the literature.

AI in education (AIEd)

The birth of AI goes back to the 1950s when John McCarthy organised a two-month workshop at Dartmouth College in the USA. In the workshop proposal, McCarthy used the term artificial intelligence for the first time in 1956 (Russel & Norvig, 2010 , p. 17):

The study [of artificial intelligence] is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.

Baker and Smith ( 2019 ) provide a broad definition of AI: “Computers which perform cognitive tasks, usually associated with human minds, particularly learning and problem-solving” (p. 10). They explain that AI does not describe a single technology. It is an umbrella term to describe a range of technologies and methods, such as machine learning, natural language processing, data mining, neural networks or an algorithm.

AI and machine learning are often mentioned in the same breath. Machine learning is a method of AI for supervised and unsupervised classification and profiling, for example to predict the likelihood of a student to drop out from a course or being admitted to a program, or to identify topics in written assignments. Popenici and Kerr ( 2017 ) define machine learning “as a subfield of artificial intelligence that includes software able to recognise patterns, make predictions, and apply newly discovered patterns to situations that were not included or covered by their initial design” (p. 2).

The concept of rational agents is central to AI: “An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through actuators” (Russel & Norvig, 2010 , p. 34). The vacuum-cleaner robot is a very simple form of an intelligent agent, but things become very complex and open-ended when we think about an automated taxi.

Experts in the field distinguish between weak and strong AI (see Russel & Norvig, 2010 , p. 1020) or narrow and general AI (see Baker & Smith, 2019 , p. 10). A philosophical question remains whether machines will be able to actually think or even develop consciousness in the future, rather than just simulating thinking and showing rational behaviour. It is unlikely that such strong or general AI will exist in the near future. We are therefore dealing here with GOFAI (“ good old-fashioned AI ”, a term coined by the philosopher John Haugeland, 1985 ) in higher education – in the sense of agents and information systems that act as if they were intelligent.

Given this understanding of AI, what are potential areas of AI applications in education, and higher education in particular? Luckin, Holmes, Griffiths, and Forcier ( 2016 ) describe three categories of AI software applications in education that are available today: a) personal tutors, b) intelligent support for collaborative learning, and c) intelligent virtual reality.

Intelligent tutoring systems (ITS) can be used to simulate one-to-one personal tutoring. Based on learner models, algorithms and neural networks, they can make decisions about the learning path of an individual student and the content to select, provide cognitive scaffolding and help, to engage the student in dialogue. ITS have enormous potential, especially in large-scale distance teaching institutions, which run modules with thousands of students, where human one-to-one tutoring is impossible. A vast array of research shows that learning is a social exercise; interaction and collaboration are at the heart of the learning process (see for example Jonassen, Davidson, Collins, Campbell, & Haag, 1995 ). However, online collaboration has to be facilitated and moderated (Salmon, 2000 ). AIEd can contribute to collaborative learning by supporting adaptive group formation based on learner models, by facilitating online group interaction or by summarising discussions that can be used by a human tutor to guide students towards the aims and objectives of a course. Finally, also drawing on ITS, intelligent virtual reality (IVR) is used to engage and guide students in authentic virtual reality and game-based learning environments. Virtual agents can act as teachers, facilitators or students’ peers, for example, in virtual or remote labs (Perez et al., 2017 ).

With the advancement of AIEd and the availability of (big) student data and learning analytics, Luckin et al. ( 2016 ) claim a “[r] enaissance in assessment” (p. 35). AI can provide just-in-time feedback and assessment. Rather than stop-and-test, AIEd can be built into learning activities for an ongoing analysis of student achievement. Algorithms have been used to predict the probability of a student failing an assignment or dropping out of a course with high levels of accuracy (e.g. Bahadır, 2016 ).

In their recent report, Baker and Smith ( 2019 ) approach educational AI tools from three different perspectives; a) learner-facing, b) teacher-facing, and c) system-facing AIEd. Learner-facing AI tools are software that students use to learn a subject matter, i.e. adaptive or personalised learning management systems or ITS. Teacher-facing systems are used to support the teacher and reduce his or her workload by automating tasks such as administration, assessment, feedback and plagiarism detection. AIEd tools also provide insight into the learning progress of students so that the teacher can proactively offer support and guidance where needed. System-facing AIEd are tools that provide information for administrators and managers on the institutional level, for example to monitor attrition patterns across faculties or colleges.

In the context of higher education, we use the concept of the student life-cycle (see Reid, 1995 ) as a framework to describe the various AI based services on the broader institutional and administrative level, as well as for supporting the academic teaching and learning process in the narrower sense.

The purpose of a systematic review is to answer specific questions, based on an explicit, systematic and replicable search strategy, with inclusion and exclusion criteria identifying studies to be included or excluded (Gough, Oliver & Thomas, 2017 ). Data is then coded and extracted from included studies, in order to synthesise findings and to shine light on their application in practice, as well as on gaps or contradictions. This contribution maps 146 articles on the topic of artificial intelligence in higher education.

Search strategy

The initial search string (see Table  1 ) and criteria (see Table  2 ) for this systematic review included peer-reviewed articles in English, reporting on artificial intelligence within education at any level, and indexed in three international databases; EBSCO Education Source, Web of Science and Scopus (covering titles, abstracts, and keywords). Whilst there are concerns about peer-review processes within the scientific community (e.g., Smith, 2006 ), articles in this review were limited to those published in peer-reviewed journals, due to their general trustworthiness in academia and the rigorous review processes undertaken (Nicholas et al., 2015 ). The search was undertaken in November 2018, with an initial 2656 records identified.

After duplicates were removed, it was decided to limit articles to those published during or after 2007, as this was the year that iPhone’s Siri was introduced; an algorithm-based personal assistant, started as an artificial intelligence project funded by the US Defense Advanced Research Projects Agency (DARPA) in 2001, turned into a company that was acquired by Apple Inc. It was also decided that the corpus would be limited to articles discussing applications of artificial intelligence in higher education only.

Screening and inter-rater reliability

The screening of 1549 titles and abstracts was carried out by a team of three coders and at this first screening stage, there was a requirement of sensitivity rather than specificity, i.e. papers were included rather than excluded. In order to reach consensus, the reasons for inclusion and exclusion for the first 80 articles were discussed at regular meetings. Twenty articles were randomly selected to evaluate the coding decisions of the three coders (A, B and C) to determine inter-rater reliability using Cohen’s kappa (κ) (Cohen, 1960 ), which is a coefficient for the degree of consistency among raters, based on the number of codes in the coding scheme (Neumann, 2007 , p. 326). Kappa values of .40–.60 are characterised as fair, .60 to .75 as good, and over .75 as excellent (Bakeman & Gottman, 1997 ; Fleiss, 1981 ). Coding consistency for inclusion or exclusion of articles between rater A and B was κ = .79, between rater A and C it was κ = .89, and between rater B and C it was κ = .69 (median = .79). Therefore, inter-rater reliability can be considered as excellent for the coding of inclusion and exclusion criteria.

After initial screening, 332 potential articles remained for screening on full text (see Fig.  1 ). However, 41 articles could not be retrieved, either through the library order scheme or by contacting authors. Therefore, 291 articles were retrieved, screened and coded, and following the exclusion of 149 papers, 146 articles remained for synthesis. Footnote 5

PRISMA diagram (slightly modified after Brunton & Thomas, 2012 , p. 86; Moher, Liberati, Tetzlaff, & Altman, 2009 , p. 8)

Coding, data extraction and analysis

In order to extract the data, all articles were uploaded into systematic review software EPPI Reviewer Footnote 6 and a coding system was developed. Codes included article information (year of publication, journal name, countries of authorship, discipline of first author), study design and execution (empirical or descriptive, educational setting) and how artificial intelligence was used (applications in the student life cycle, specific applications and methods). Articles were also coded on whether challenges and benefits of AI were present, and whether AI was defined. Descriptive data analysis was carried out with the statistics software R using the tidyr package (Wickham & Grolemund, 2016 ).

Limitations

Whilst this systematic review was undertaken as rigorously as possible, each review is limited by its search strategy. Although the three educational research databases chosen are large and international in scope, by applying the criteria of peer-reviewed articles published only in English or Spanish, research published on AI in other languages were not included in this review. This also applies to research in conference proceedings, book chapters or grey literature, or those articles not published in journals that are indexed in the three databases searched. In addition, although Spanish peer-reviewed articles were added according to inclusion criteria, no specific search string in the language was included, which narrows down the possibility of including Spanish papers that were not indexed with the chosen keywords. Future research could consider using a larger number of databases, publication types and publication languages, in order to widen the scope of the review. However, serious consideration would then need to be given to project resources and the manageability of the review (see Authors, in press).

Journals, authorship patterns and methods

Articles per year.

There was a noticeable increase in the papers published from 2007 onwards. The number of included articles grew from six in 2007 to 23 in 2018 (see Fig.  2 ).

Number of included articles per year ( n  = 146)

The papers included in the sample were published in 104 different journals. The greatest number of articles were published in the International Journal of Artificial Intelligence in Education ( n  = 11) , followed by Computers & Education ( n  = 8) , and the International Journal of Emerging Technologies in Learning ( n  = 5) . Table  3 lists 19 journals that published at least two articles on AI in higher education from 2007 to 2018.

For the geographical distribution analysis of articles, the country of origin of the first author was taken into consideration ( n  = 38 countries). Table 4 shows 19 countries that contributed at least two papers, and it reveals that 50% of all articles come from only four countries: USA, China, Taiwan, and Turkey.

Author affiliations

Again, the affiliation of the first author was taken into consideration (see Table 5 ). Researchers working in departments of Computer Science contributed by far the greatest number of papers ( n  = 61) followed by Science, Technology, Engineering and Mathematics (STEM) departments ( n  = 29). Only nine first authors came from an Education department, some reported dual affiliation with Education and Computer Science ( n  = 2), Education and Psychology ( n  = 1), or Education and STEM ( n  = 1).

Thus, 13 papers (8.9%) were written by first authors with an Education background. It is noticeable that three of them were contributed by researchers from the Teachers College at Columbia University, New York, USA (Baker, 2016 ; Paquette, Lebeau, Beaulieu, & Mayers, 2015 ; Perin & Lauterbach, 2018 ) – and they were all published in the same journal, i.e. the International Journal of Artificial Intelligence in Education .

Thirty studies (20.5%) were coded as being theoretical or descriptive in nature. The vast majority of studies (73.3%) applied quantitative methods, whilst only one (0.7%) was qualitative in nature and eight (5.5%) followed a mixed-methods approach. The purpose of the qualitative study, involving interviews with ESL students, was to explore the nature of written feedback coming from an automated essay scoring system compared to a human teacher (Dikli, 2010 ). In many cases, authors employed quasi-experimental methods, being an intentional sample divided into the experimental group, where an AI application (e.g. an intelligent tutoring system) was applied, and the control group without the intervention, followed by pre- and posttest (e.g. Adamson, Dyke, Jang, & Rosé, 2014 ).

Understanding of AI and critical reflection of challenges and risks

There are many different types and levels of AI mentioned in the articles, however only five out of 146 included articles (3.4%) provide an explicit definition of the term “Artificial Intelligence”. The main characteristics of AI, described in all five studies, are the parallels between the human brain and artificial intelligence. The authors conceptualise AI as intelligent computer systems or intelligent agents with human features, such as the ability to memorise knowledge, to perceive and manipulate their environment in a similar way as humans, and to understand human natural language (see Huang, 2018 ; Lodhi, Mishra, Jain, & Bajaj, 2018 ; Welham, 2008 ). Dodigovic ( 2007 ) defines AI in her article as follows (p. 100):

Artificial intelligence (AI) is a term referring to machines which emulate the behaviour of intelligent beings [ … ] AI is an interdisciplinary area of knowledge and research, whose aim is to understand how the human mind works and how to apply the same principles in technology design. In language learning and teaching tasks, AI can be used to emulate the behaviour of a teacher or a learner [ … ] . (p. 100)

Dodigovic is the only author who gives a definition of AI, and comes from an Arts, Humanities and Social Science department, taking into account aspects of AI and intelligent tutors in second language learning.

A stunningly low number of authors, only two out of 146 articles (1.4%), critically reflect upon ethical implications, challenges and risks of applying AI in education. Li ( 2007 ) deals with privacy concerns in his article about intelligent agent supported online learning:

Privacy is also an important concern in applying agent-based personalised education. As discussed above, agents can autonomously learn many of students’ personal information, like learning style and learning capability. In fact, personal information is private. Many students do not want others to know their private information, such as learning styles and/or capabilities. Students might show concern over possible discrimination from instructors in reference to learning performance due to special learning needs. Therefore, the privacy issue must be resolved before applying agent-based personalised teaching and learning technologies. (p. 327)

Another challenge of applying AI is mentioned by Welham ( 2008 , p. 295) concerning the costs and time involved in developing and introducing AI-based methods that many public educational institutions cannot afford.

AI applications in higher education

As mentioned before, we used the concept of the student life-cycle (see Reid, 1995 ) as a framework to describe the various AI based services at the institutional and administrative level (e.g. admission, counselling, library services), as well as at the academic support level for teaching and learning (e.g. assessment, feedback, tutoring). Ninety-two studies (63.0%) were coded as relating to academic support services and 48 (32.8%) as administrative and institutional services; six studies (4.1%) covered both levels. The majority of studies addressed undergraduate students ( n  = 91, 62.3%) compared to 11 (7.5%) focussing on postgraduate students, and another 44 (30.1%) that did not specify the study level.

The iterative coding process led to the following four areas of AI applications with 17 sub-categories, covered in the publications: a) adaptive systems and personalisation, b) assessment and evaluation, c) profiling and prediction, and d) intelligent tutoring systems. Some studies addressed AI applications in more than one area (see Table  6 ).

The nature and scope of the various AI applications in higher education will be described along the lines of these four application categories in the following synthesis.

Profiling and prediction

The basis for many AI applications are learner models or profiles that allow prediction, for example of the likelihood of a student dropping out of a course or being admitted to a programme, in order to offer timely support or to provide feedback and guidance in content related matters throughout the learning process. Classification, modelling and prediction are an essential part of educational data mining (Phani Krishna, Mani Kumar, & Aruna Sri, 2018 ).

Most of the articles (55.2%, n  = 32) address issues related to the institutional and administrative level, many (36.2%, n  = 21) are related to academic teaching and learning at the course level, and five (8.6%) are concerned with both levels. Articles dealing with profiling and prediction were classified into three sub-categories; admission decisions and course scheduling ( n  = 7), drop-out and retention ( n  = 23), and student models and academic achievement ( n  = 27). One study that does not fall into any of these categories is the study by Ge and Xie ( 2015 ), which is concerned with forecasting the costs of a Chinese university to support management decisions based on an artificial neural network.

All of the 58 studies in this area applied machine learning methods, to recognise and classify patterns, and to model student profiles to make predictions. Thus, they are all quantitative in nature. Many studies applied several machine learning algorithms (e.g. ANN, SVM, RF, NB; see Table  7 ) Footnote 7 and compared their overall prediction accuracy with conventional logistic regression. Table 7 shows that machine learning methods outperformed logistic regression in all studies in terms of their classification accuracy in percent. To evaluate the performance of classifiers, the F1-score can also be used, which takes into account the number of positive instances correctly classified as positive, the number of negative instances incorrectly classified as positive, and the number of positive instances incorrectly classified as negative (Umer et al., 2017 ; for a brief overview of measures of diagnostic accuracy, see Šimundić, 2009 ). The F1-score ranges between 0 and 1 with its best value at 1 (perfect precision and recall). Yoo and Kim ( 2014 ) reported high F1-scores of 0.848, 0.911, and 0.914 for J48, NB, and SVM, in a study to predict student’s group project performance from online discussion participation.

Admission decisions and course scheduling

Chen and Do ( 2014 ) point out that “the accurate prediction of students’ academic performance is of importance for making admission decisions as well as providing better educational services” (p. 18). Four studies aimed to predict whether or not a prospective student would be admitted to university. For example, Acikkar and Akay ( 2009 ) selected candidates for a School of Physical Education and Sports in Turkey based on a physical ability test, their scores in the National Selection and Placement Examination, and their graduation grade point average (GPA). They used the support vector machine (SVM) technique to classify the students and where able to predict admission decisions on a level of accuracy of 97.17% in 2006 and 90.51% in 2007. SVM was also applied by Andris, Cowen, and Wittenbach ( 2013 ) to find spatial patterns that might favour prospective college students from certain geographic regions in the USA. Feng, Zhou, and Liu ( 2011 ) analysed enrolment data from 25 Chinese provinces as the training data to predict registration rates in other provinces using an artificial neural network (ANN) model. Machine learning methods and ANN are also used to predict student course selection behaviour to support course planning. Kardan, Sadeghi, Ghidary, and Sani ( 2013 ) investigated factors influencing student course selection, such as course and instructor characteristics, workload, mode of delivery and examination time, to develop a model to predict course selection with an ANN in two Computer Engineering and Information Technology Masters programs. In another paper from the same author team, a decision support system for course offerings was proposed (Kardan & Sadeghi, 2013 ). Overall, the research shows that admission decisions can be predicted at high levels of accuracy, so that an AI solution could relieves the administrative staff and allows them to focus on the more difficult cases.

Drop-out and retention

Studies pertaining to drop-out and retention are intended to develop early warning systems to detect at-risk students in their first year (e.g., Alkhasawneh & Hargraves, 2014 ; Aluko, Adenuga, Kukoyi, Soyingbe, & Oyedeji, 2016 ; Hoffait & Schyns, 2017 ; Howard, Meehan, & Parnell, 2018 ) or to predict the attrition of undergraduate students in general (e.g., Oztekin, 2016 ; Raju & Schumacker, 2015 ). Delen ( 2011 ) used institutional data from 25,224 students enrolled as Freshmen in an American university over 8 years. In this study, three classification techniques were used to predict dropout: ANN, decision trees (DT) and logistic regression. The data contained variables related to students’ demographic, academic, and financial characteristics (e.g. age, sex, ethnicity, GPA, TOEFL score, financial aid, student loan, etc.). Based on a 10-fold cross validation, Delen ( 2011 ) found that the ANN model worked best with an accuracy rate of 81.19% (see Table 7 ) and he concluded that the most important predictors of student drop-out are related to the student’s past and present academic achievement, and whether they receive financial support. Sultana, Khan, and Abbas ( 2017 , p. 107) discussed the impact of cognitive and non-cognitive features of students for predicting academic performance of undergraduate engineering students. In contrast to many other studies, they focused on non-cognitive variables to improve prediction accuracy, i.e. time management, self-concept, self-appraisal, leadership, and community support.

Student models and academic achievement

Many more studies are concerned with profiling students and modelling learning behaviour to predict their academic achievements at the course level. Hussain et al. ( 2018 ) applied several machine learning algorithms to analyse student behavioural data from the virtual learning environment at the Open University UK, in order to predict student engagement, which is of particular importance at a large scale distance teaching university, where it is not possible to engage the majority of students in face-to-face sessions. The authors aim to develop an intelligent predictive system that enables instructors to automatically identify low-engaged students and then to make an intervention. Spikol, Ruffaldi, Dabisias, and Cukurova ( 2018 ) used face and hand tracking in workshops with engineering students to estimate success in project-based learning. They concluded that results generated from multimodal data can be used to inform teachers about key features of project-based learning activities. Blikstein et al. ( 2014 ) investigated patterns of how undergraduate students learn computer programming, based on over 150,000 code transcripts that the students created in software development projects. They found that their model, based on the process of programming, had better predictive power than the midterm grades. Another example is the study of Babić ( 2017 ), who developed a model to predict student academic motivation based on their behaviour in an online learning environment.

The research on student models is an important foundation for the design of intelligent tutoring systems and adaptive learning environments.

• Intelligent tutoring systems

All of the studies investigating intelligent tutoring systems (ITS) ( n  = 29) are only concerned with the teaching and learning level, except for one that is contextualised at the institutional and administrative level. The latter presents StuA , an interactive and intelligent student assistant that helps newcomers in a college by answering queries related to faculty members, examinations, extra curriculum activities, library services, etc. (Lodhi et al., 2018 ).

The most common terms for referring to ITS described in the studies are intelligent (online) tutors or intelligent tutoring systems (e.g., in Dodigovic, 2007 ; Miwa, Terai, Kanzaki, & Nakaike, 2014 ), although they are also identified often as intelligent (software) agents (e.g., Schiaffino, Garcia, & Amandi, 2008 ), or intelligent assistants (e.g., in Casamayor, Amandi, & Campo, 2009 ; Jeschike, Jeschke, Pfeiffer, Reinhard, & Richter, 2007 ). According to Welham ( 2008 ), the first ITS reported was the SCHOLAR system, launched in 1970, which allowed the reciprocal exchange of questions between teacher and student, but not holding a continuous conversation.

Huang and Chen ( 2016 , p. 341) describe the different models that are usually integrated in ITS: the student model (e.g. information about the student’s knowledge level, cognitive ability, learning motivation, learning styles), the teacher model (e.g. analysis of the current state of students, select teaching strategies and methods, provide help and guidance), the domain model (knowledge representation of both students and teachers) and the diagnosis model (evaluation of errors and defects based on domain model).

The implementation and validation of the ITS presented in the studies usually took place over short-term periods (a course or a semester) and no longitudinal studies were identified, except for the study by Jackson and Cossitt ( 2015 ). On the other hand, most of the studies showed (sometimes slightly) positive / satisfactory preliminary results regarding the performance of the ITS, but they did not take into account the novelty effect that a new technological development could have in an educational context. One study presented negative results regarding the type of support that the ITS provided (Adamson et al., 2014 ), which could have been more useful if it was more adjusted to the type of (in this case, more advanced) learners.

Overall, more research is needed on the effectiveness of ITS. The last meta-analysis of 39 ITS studies was published over 5 years ago: Steenbergen-Hu and Cooper ( 2014 ) found that ITS had a moderate effect of students’ learning, and that ITS were less effective that human tutoring, but ITS outperformed all other instruction methods (such as traditional classroom instruction, reading printed or digital text, or homework assignments).

The studies addressing various ITS functions were classified as follows: teaching course content ( n  = 12), diagnosing strengths or gaps in students’ knowledge and providing automated feedback ( n  = 7), curating learning materials based on students’ needs ( n  = 3), and facilitating collaboration between learners ( n  = 2).

Teaching course content

Most of the studies ( n  = 4) within this group focused on teaching Computer Science content (Dobre, 2014 ; Hooshyar, Ahmad, Yousefi, Yusop, & Horng, 2015 ; Howard, Jordan, di Eugenio, & Katz, 2017 ; Shen & Yang, 2011 ). Other studies included ITS teaching content for Mathematics (Miwa et al., 2014 ), Business Statistics and Accounting (Jackson & Cossitt, 2015 ; Palocsay & Stevens, 2008 ), Medicine (Payne et al., 2009 ) and writing and reading comprehension strategies for undergraduate Psychology students (Ray & Belden, 2007 ; Weston-Sementelli, Allen, & McNamara, 2018 ). Overall, these ITS focused on providing teaching content to students and, at the same time, supporting them by giving adaptive feedback and hints to solve questions related to the content, as well as detecting students’ difficulties/errors when working with the content or the exercises. This is made possible by monitoring students’ actions with the ITS.

In the study by Crown, Fuentes, Jones, Nambiar, and Crown ( 2011 ), a combination of teaching content through dialogue with a chatbot, that at the same time learns from this conversation - defined as a text-based conversational agent -, is described, which moves towards a more active, reflective and thinking student-centred learning approach. Duffy and Azevedo ( 2015 ) present an ITS called MetaTutor, which is designed to teach students about the human circulatory system, but it also puts emphasis on supporting students’ self-regulatory processes assisted by the features included in the MetaTutor system (a timer, a toolbar to interact with different learning strategies, and learning goals, amongst others).

Diagnosing strengths or gaps in student knowledge, and providing automated feedback

In most of the studies ( n  = 4) of this group, ITS are presented as a rather one-way communication from computer to student, concerning the gaps in students’ knowledge and the provision of feedback. Three examples in the field of STEM have been found: two of them where the virtual assistance is presented as a feature in virtual laboratories by tutoring feedback and supervising student behaviour (Duarte, Butz, Miller, & Mahalingam, 2008 ; Ramírez, Rico, Riofrío-Luzcando, Berrocal-Lobo, & Antonio, 2018 ), and the third one is a stand-alone ITS in the field of Computer Science (Paquette et al., 2015 ). One study presents an ITS of this kind in the field of second language learning (Dodigovic, 2007 ).

In two studies, the function of diagnosing mistakes and the provision of feedback is accomplished by a dialogue between the student and the computer. For example, with an interactive ubiquitous teaching robot that bases its speech on question recognition (Umarani, Raviram, & Wahidabanu, 2011 ), or with the tutoring system, based on a tutorial dialogue toolkit for introductory college Physics (Chi, VanLehn, Litman, & Jordan, 2011 ). The same tutorial dialogue toolkit (TuTalk) is the core of the peer dialogue agent presented by Howard et al. ( 2017 ), where the ITS engages in a one-on-one problem-solving peer interaction with a student and can interact verbally, graphically and in a process-oriented way, and engage in collaborative problem solving instead of tutoring. This last study could be considered as part of a new category regarding peer-agent collaboration.

Curating learning materials based on student needs

Two studies focused on this kind of ITS function (Jeschike et al., 2007 ; Schiaffino et al., 2008 ), and a third one mentions it in a more descriptive way as a feature of the detection system presented (Hall Jr & Ko, 2008 ). Schiaffino et al. ( 2008 ) present eTeacher as a system for personalised assistance to e-learning students by observing their behaviour in the course and generating a student’s profile. This enables the system to provide specific recommendations regarding the type of reading material and exercises done, as well as personalised courses of action. Jeschike et al. ( 2007 ) refers to an intelligent assistant contextualised in a virtual laboratory of statistical mechanics, where it presents exercises and the evaluation of the learners’ input to content, and interactive course material that adapts to the learner.

Facilitating collaboration between learners

Within this group we can identify only two studies: one focusing on supporting online collaborative learning discussions by using academically productive talk moves (Adamson et al., 2014 ); and the second one, on facilitating collaborative writing by providing automated feedback, generated automatic questions, and the analysis of the process (Calvo, O’Rourke, Jones, Yacef, & Reimann, 2011 ). Given the opportunities that the applications described in these studies afford for supporting collaboration among students, more research in this area would be desireable.

The teachers’ perspective

As mentioned above, Baker and Smith ( 2019 , p.12) distinguish between student and teacher-facing AI. However, only two included articles in ITS focus on the teacher’s perspective. Casamayor et al. ( 2009 ) focus on assisting teachers with the supervision and detection of conflictive cases in collaborative learning. In this study, the intelligent assistant provides the teachers with a summary of the individual progress of each group member and the type of participation each of them have had in their work groups, notification alerts derived from the detection of conflict situations, and information about the learning style of each student-logging interactions, so that the teachers can intervene when they consider it convenient. The other study put the emphasis on the ITS sharing teachers’ tutoring tasks by providing immediate feedback (automating tasks), and leaving the teachers the role of providing new hints and the correct solution to the tasks (Chou, Huang, & Lin, 2011 ). The study of Chi et al. ( 2011 ) also mentions the ITS purpose to share teacher’s tutoring tasks. The main aim in any of these cases is to reduce teacher’s workload. Furthermore, many of the learner-facing studies deal with the teacher-facing functions too, although they do not put emphasis on the teacher’s perspective.

Assessment and evaluation

Assessment and evaluation studies also largely focused on the level of teaching and learning (86%, n  = 31), although five studies described applications at the institutional level. In order to gain an overview of student opinion about online and distance learning at their institution, academics at Anadolu University (Ozturk, Cicek, & Ergul, 2017 ) used sentiment analysis to analyse mentions by students on Twitter, using Twitter API Twython and terms relating to the system. This analysis of publicly accessible data, allowed researchers insight into student opinion, which otherwise may not have been accessible through their institutional LMS, and which can inform improvements to the system. Two studies used AI to evaluate student Prior Learning and Recognition (PLAR); Kalz et al. ( 2008 ) used Latent Semantic Analysis and ePortfolios to inform personalised learning pathways for students, and Biletska, Biletskiy, Li, and Vovk ( 2010 ) used semantic web technologies to convert student credentials from different institutions, which could also provide information from course descriptions and topics, to allow for easier granting of credit. The final article at the institutional level (Sanchez et al., 2016 ) used an algorithm to match students to professional competencies and capabilities required by companies, in order to ensure alignment between courses and industry needs.

Overall, the studies show that AI applications can perform assessment and evaluation tasks at very high accuracy and efficiency levels. However, due to the need to calibrate and train the systems (supervised machine learning), they are more applicable to courses or programs with large student numbers.

Articles focusing on assessment and evaluation applications of AI at the teaching and learning level, were classified into four sub-categories; automated grading ( n  = 13), feedback ( n  = 8), evaluation of student understanding, engagement and academic integrity ( n  = 5), and evaluation of teaching ( n  = 5).

Automated grading

Articles that utilised automated grading, or Automated Essay Scoring (AES) systems, came from a range of disciplines (e.g. Biology, Medicine, Business Studies, English as a Second Language), but were mostly focused on its use in undergraduate courses ( n  = 10), including those with low reading and writing ability (Perin & Lauterbach, 2018 ). Gierl, Latifi, Lai, Boulais, and Champlain’s ( 2014 ) use of open source Java software LightSIDE to grade postgraduate medical student essays resulted in an agreement between the computer classification and human raters between 94.6% and 98.2%, which could enable reducing cost and the time associated with employing multiple human assessors for large-scale assessments (Barker, 2011 ; McNamara, Crossley, Roscoe, Allen, & Dai, 2015 ). However, they stressed that not all writing genres may be appropriate for AES and that it would be impractical to use in most small classrooms, due to the need to calibrate the system with a large number of pre-scored assessments. The benefits of using algorithms that find patterns in text responses, however, has been found to lead to encouraging more revisions by students (Ma & Slater, 2015 ) and to move away from merely measuring student knowledge and abilities by multiple choice tests (Nehm, Ha, & Mayfield, 2012 ). Continuing issues persist, however, in the quality of feedback provided by AES (Dikli, 2010 ), with Barker ( 2011 ) finding that the more detailed the feedback provided was, the more likely students were to question their grades, and a question was raised over the benefits of this feedback for beginning language students (Aluthman, 2016 ).

Articles concerned with feedback included a range of student-facing tools, including intelligent agents that provide students with prompts or guidance when they are confused or stalled in their work (Huang, Chen, Luo, Chen, & Chuang, 2008 ), software to alert trainee pilots when they are losing situation awareness whilst flying (Thatcher, 2014 ), and machine learning techniques with lexical features to generate automatic feedback and assist in improving student writing (Chodorow, Gamon, & Tetreault, 2010 ; Garcia-Gorrostieta, Lopez-Lopez, & Gonzalez-Lopez, 2018 ; Quixal & Meurers, 2016 ), which can help reduce students cognitive overload (Yang, Wong, & Yeh, 2009 ). The automated feedback system based on adaptive testing reported by Barker ( 2010 ), for example, not only determines the most appropriate individual answers according to Bloom’s cognitive levels, but also recommends additional materials and challenges.

Evaluation of student understanding, engagement and academic integrity

Three articles reported on student-facing tools that evaluate student understanding of concepts (Jain, Gurupur, Schroeder, & Faulkenberry, 2014 ; Zhu, Marquez, & Yoo, 2015 ) and provide personalised assistance (Samarakou, Fylladitakis, Früh, Hatziapostolou, & Gelegenis, 2015 ). Hussain et al. ( 2018 ) used machine learning algorithms to evaluate student engagement in a social science course at the Open University, including final results, assessment scores and the number of clicks that students make in the VLE, which can alert instructors to the need for intervention, and Amigud, Arnedo-Moreno, Daradoumis, and Guerrero-Roldan ( 2017 ) used machine learning algorithms to check academic integrity, by assessing the likelihood of student work being similar to their other work. With a mean accuracy of 93%, this opens up possibilities of reducing the need for invigilators or to access student accounts, thereby reducing concerns surrounding privacy.

Evaluation of teaching

Four studies used data mining algorithms to evaluate lecturer performance through course evaluations (Agaoglu, 2016 ; Ahmad & Rashid, 2016 ; DeCarlo & Rizk, 2010 ; Gutierrez, Canul-Reich, Ochoa Zezzatti, Margain, & Ponce, 2018 ), with Agaoglu ( 2016 ) finding, through using four different classification techniques, that many questions in the evaluation questionnaire were irrelevant. The application of an algorithm to evaluate the impact of teaching methods in a differential equations class, found that online homework with immediate feedback was more effective than clickers (Duzhin & Gustafsson, 2018 ). The study also found that, whilst previous exam results are generally good predictors for future exam results, they say very little about students’ expected performance in project-based tasks.

Adaptive systems and personalisation

Most of the studies on adaptive systems (85%, n  = 23) are situated at the teaching and learning level, with four cases considering the institutional and administrative level. Two studies explored undergraduate students’ academic advising (Alfarsi, Omar, & Alsinani, 2017 ; Feghali, Zbib, & Hallal, 2011 ), and Nguyen et al. ( 2018 ) focused on AI to support university career services. Ng, Wong, Lee, and Lee ( 2011 ) reported on the development of an agent-based distance LMS, designed to manage resources, support decision making and institutional policy, and assist with managing undergraduate student study flow (e.g. intake, exam and course management), by giving users access to data across disciplines, rather than just individual faculty areas.

There does not seem to be agreement within the studies on a common term for adaptive systems, and that is probably due to the diverse functions they carry out, which also supports the classification of studies. Some of those terms coincide in part with the ones used for ITS, e.g. intelligent agents (Li, 2007 ; Ng et al., 2011 ). The most general terms used are intelligent e-learning system (Kose & Arslan, 2016 ), adaptive web-based learning system (Lo, Chan, & Yeh, 2012 ), or intelligent teaching system (Yuanyuan & Yajuan, 2014 ). As in ITS, most of the studies either describe the system or include a pilot study but no longer-term results are reported. Results from these pilot studies are usually reported as positive, except in Vlugter, Knott, McDonald, and Hall ( 2009 ), where the experimental group that used the dialogue-based computer assisted language-system scored lower than the control group in the delayed post-tests.

The 23 studies focused on teaching and learning can be classified into five sub-categories; teaching course content ( n  = 7), recommending/providing personalised content ( n  = 5), supporting teachers in learning and teaching design ( n  = 3), using academic data to monitor and guide students ( n  = 2), and supporting representation of knowledge using concept maps ( n  = 2). However, some studies were difficult to classify, due to their specific and unique functions; helping to organise online learning groups with similar interests (Yang, Wang, Shen, & Han, 2007 ), supporting business decisions through simulation (Ben-Zvi, 2012 ), or supporting changes in attitude and behaviour for patients with Anorexia Nervosa, through embodied conversational agents (Sebastian & Richards, 2017 ). Aparicio et al. ( 2018 ) present a study where no adaptive system application was analysed, rather students’ perceptions of the use of information systems in education in general - and biomedical education in particular - were analysed, including intelligent information access systems .

The disciplines that are taught through adaptive systems are diverse, including environmental education (Huang, 2018 ), animation design (Yuanyuan & Yajuan, 2014 ), language learning (Jia, 2009 ; Vlugter et al., 2009 ), Computer Science (Iglesias, Martinez, Aler, & Fernandez, 2009 ) and Biology (Chaudhri et al., 2013 ). Walsh, Tamjidul, and Williams ( 2017 ), however, present an adaptive system based on machine learning-human machine learning symbiosis from a descriptive perspective, without specifying any discipline.

Recommending/providing personalised content

This group refers to adaptive systems that deliver customised content, materials and exercises according to students’ behaviour profiling in Business and Administration studies (Hall Jr & Ko, 2008 ) and Computer Science (Kose & Arslan, 2016 ; Lo et al., 2012 ). On the other hand, Tai, Wu, and Li ( 2008 ) present an e-learning recommendation system for online students to help them choose among courses, and Torres-Díaz, Infante Moro, and Valdiviezo Díaz ( 2014 ) emphasise the usefulness of (adaptive) recommendation systems in MOOCs to suggest actions, new items and users, according to students’ personal preferences.

Supporting teachers in learning and teaching design

In this group, three studies were identified. One study puts the emphasis on a hybrid recommender system of pedagogical patterns, to help teachers define their teaching strategies, according to the context of a specific class (Cobos et al., 2013 ), and another study presents a description of a metadata-based model to implement automatic learning designs that can solve detected problems (Camacho & Moreno, 2007 ). Li’s ( 2007 ) descriptive study argues that intelligent agents save time for online instructors, by leaving the most repetitive tasks to the systems, so that they can focus more on creative work.

Using academic data to monitor and guide students

The adaptive systems within this category focus on the extraction of student academic information to perform diagnostic tasks, and help tutors to offer a more proactive personal guidance (Rovira, Puertas, & Igual, 2017 ); or, in addition to that task, include performance evaluation and personalised assistance and feedback, such as the Learner Diagnosis, Assistance, and Evaluation System based on AI (StuDiAsE) for engineering learners (Samarakou et al., 2015 ).

Supporting representation of knowledge in concept maps

To help build students’ self-awareness of conceptual structures, concept maps can be quite useful. In the two studies of this group, an expert system was included, e.g. in order to accommodate selected peer ideas in the integrated concept maps and allow teachers to flexibly determine in which ways the selected concept maps are to be merged ( ICMSys ) (Kao, Chen, & Sun, 2010 ), or to help English as a Foreign Language college students to develop their reading comprehension through mental maps of referential identification (Yang et al., 2009 ). This latter system also includes system-guided instruction, practice and feedback.

Conclusions and implications for further educational research

In this paper, we have explored the field of AIEd research in terms of authorship and publication patterns. It is evident that US-American, Chinese, Taiwanese and Turkish colleagues (accounting for 50% of the publications as first authors) from Computer Science and STEM departments (62%) dominate the field. The leading journals are the International Journal of Artificial Intelligence in Education , Computers & Education , and the International Journal of Emerging Technologies in Learning .

More importantly, this study has provided an overview of the vast array of potential AI applications in higher education to support students, faculty members, and administrators. They were described in four broad areas (profiling and prediction, intelligent tutoring systems, assessment and evaluation, and adaptive systems and personalisation) with 17 sub-categories. This structure, which was derived from the systematic review, contributes to the understanding and conceptualisation of AIEd practice and research.

On the other hand, the lack of longitudinal studies and the substantial presence of descriptive and pilot studies from the technological perspective, as well as the prevalence of quantitative methods - especially quasi-experimental methods - in empirical studies, shows that there is still substantial room for educators to aim at innovative and meaningful research and practice with AIEd that could have learning impact within higher education, e.g. adopting design-based approaches (Easterday, Rees Lewis, & Gerber, 2018 ). A recent systematic literature review on personalisation in educational technology coincided with the predominance of experiences in technological developments, which also often used quantitative methods (Bartolomé, Castañeda, & Adell, 2018 ). Misiejuk and Wasson ( 2017 , p. 61) noted in their systematic review on Learning Analytics that “there are very few implementation studies and impact studies” (p. 61), which is also similar to the findings in the present article.

The full consequences of AI development cannot yet be foreseen today, but it seems likely that AI applications will be a top educational technology issue for the next 20 years. AI-based tools and services have a high potential to support students, faculty members and administrators throughout the student lifecycle. The applications that are described in this article provide enormous pedagogical opportunities for the design of intelligent student support systems, and for scaffolding student learning in adaptive and personalized learning environments. This applies in particular to large higher education institutions (such as open and distance teaching universities), where AIEd might help to overcome the dilemma of providing access to higher education for very large numbers of students (mass higher education). On the other hand, it might also help them to offer flexible, but also interactive and personalized learning opportunities, for example by relieving teachers from burdens, such as grading hundreds or even thousands of assignments, so that they can focus on their real task: empathic human teaching.

It is crucial to emphasise that educational technology is not (only) about technology – it is the pedagogical, ethical, social, cultural and economic dimensions of AIEd we should be concerned about. Selwyn ( 2016 , p. 106) writes:

The danger, of course, lies in seeing data and coding as an absolute rather than relative source of guidance and support. Education is far too complex to be reduced solely to data analysis and algorithms. As with digital technologies in general, digital data do not offer a neat technical fix to education dilemmas – no matter how compelling the output might be.

We should not strive for what is technically possible, but always ask ourselves what makes pedagogical sense. In China, systems are already being used to monitor student participation and expressions via face recognition in classrooms (so called Intelligent Classroom Behavior Management System, Smart Campus Footnote 8 ) and display them to the teacher on a dashboard. This is an example of educational surveillance, and it is highly questionable whether such systems provide real added value for a good teacher who should be able to capture the dynamics in a learning group (online and in an on-campus setting) and respond empathically and in a pedagogically meaningful way. In this sense, it is crucial to adopt an ethics of care (Prinsloo, 2017 ) to start thinking on how we are exploring the potential of algorithmic decision-making systems that are embedded in AIEd applications. Furthermore, we should also always remember that AI systems “first and foremost, require control by humans. Even the smartest AI systems can make very stupid mistakes. […] AI Systems are only as smart as the date used to train them” (Kaplan & Haenlein, 2019 , p. 25). Some critical voices in educational technology remind us that we should go beyond the tools, and talk again about learning and pedagogy, as well as acknowledging the human aspects of digital technology use in education (Castañeda & Selwyn, 2018 ). The new UNESCO report on challenges and opportunities of AIEd for sustainable development deals with various areas, all of which have an important pedagogical, social and ethical dimension, e.g. ensuring inclusion and equity in AIEd, preparing teachers for AI-powered education, developing quality and inclusive data systems, or ethics and transparency in data collection, use and dissemination (Pedró, Subosa, Rivas, & Valverde, 2019 ).

That being said, a stunning result of this review is the dramatic lack of critical reflection of the pedagogical and ethical implications as well as risks of implementing AI applications in higher education. Concerning ethical implications, privacy issues were also noted to be rarely addressed in empirical studies in a recent systematic review on Learning Analytics (Misiejuk & Wasson, 2017 ). More research is needed from educators and learning designers on how to integrate AI applications throughout the student lifecycle, to harness the enormous opportunities that they afford for creating intelligent learning and teaching systems. The low presence of authors affiliated with Education departments identified in our systematic review is evidence of the need for educational perspectives on these technological developments.

The lack of theory might be a syndrome within the field of educational technology in general. In a recent study, Hew, Lan, Tang, Jia, and Lo ( 2019 ) found that more than 40% of articles in three top educational technology journals were wholly a-theoretical. The systematic review by Bartolomé et al. ( 2018 ) also revealed this lack of explicit pedagogical perspectives in the studies analysed. The majority of research included in this systematic review is merely focused on analysing and finding patterns in data to develop models, and to make predictions that inform student and teacher facing applications, or to support administrative decisions using mathematical theories and machine learning methods that were developed decades ago (see Russel & Norvig, 2010 ). This kind of research is now possible through the growth of computing power and the vast availability of big digital student data. However, at this stage, there is very little evidence for the advancement of pedagogical and psychological learning theories related to AI driven educational technology. It is an important implication of this systematic review, that researchers are encouraged to be explicit about the theories that underpin empirical studies about the development and implementation of AIEd projects, in order to expand research to a broader level, helping us to understand the reasons and mechanisms behind this dynamic development that will have an enormous impact on higher education institutions in the various areas we have covered in this review.

Availability of data and materials

The datasets used and/or analysed during the current study (the bibliography of included studies) are available from the corresponding author upon request.

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my YouTube channel.

My Top AI Tools for Researchers and Academics – Tested and Reviewed!

There are many different tools now available on the market but there are only a handful that are specifically designed with researchers and academics as their primary user.

These are my recommendations that’ll cover almost everything that you’ll want to do:

Want to find out all of the tools that you could use?

Here they are, below:

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Litmaps –  https://www.litmaps.com
• Research rabbit – https://www.researchrabbit.ai/
• Connected Papers –  https://www.connectedpapers.com/
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Laser AI –  https://laser.ai/
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Consensus –  https://consensus.app/
• Iris AI –  https://iris.ai/
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Aetherbrain – https://aetherbrain.ai
• Explain Paper – https://www.explainpaper.com
• Chat PDF – https://www.chatpdf.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/
• Open Read –  https://www.openread.academy

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Jenny.AI – https://jenni.ai/ (20% off with code ANDY20)
• Yomu – https://www.yomu.ai
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• PaperPal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Best free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

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The evolution of artificial intelligence (AI) spending by the U.S. government

Subscribe to the center for technology innovation newsletter, jacob larson , jacob larson researcher - w.p. carey school of business, arizona state university james s. denford , james s. denford professor, management department - royal military college of canada gregory s. dawson , and gregory s. dawson clinical professor, the w. p. carey school of business - arizona state university kevin c. desouza kevin c. desouza nonresident senior fellow - governance studies , center for technology innovation @kevdesouza.

March 26, 2024

• In concert with advancements in artificial intelligence technology, the U.S. federal government’s investments in AI have rapidly accelerated in recent years.
• The authors analyze hundreds of federal contracts related to AI, and find a tremendous growth in contracts in the last year especially.
• Notably, 2022-2023 saw massive new investment from the Department of Defense (DoD) in AI-related contracts.

In April 2023, a Stanford study found rapid acceleration in the U.S. federal government spending in 2022. In parallel, the House Appropriations Committee was reported in June 2023 to be focusing on advancing legislation to incorporate artificial intelligence (AI) in an increasing number of programs and third-party reports tracking the progress of this legislation corroborates those findings. In November 2023, both the Department of Defense (DoD) and the Department of State (DoS) released AI strategies, illustrating that policy is starting to catch up to, and potentially shape, expenditures. Recognizing this criticality of this domain on government, The Brookings Institution’s Artificial Intelligence and Emerging Technology Initiative (AIET) has been established to advance good governance of transformative new technologies to promote effective solutions to the most pressing challenges posed by AI and emerging technologies.

In this second in a series of articles on AI spending in the U.S. federal government, we continue to follow the trail of money to understand the federal market for AI work. In our last article , we analyzed five years of federal contracts. Key findings included that over 95% of AI-labeled expenditures were in NAICS 54 (professional, scientific, and technical services); that within this category over half of the contracts and nearly 90% of contract value sit within the Department of Defense; and that the vast majority of vendors had a single contract, reflecting a very fragmented vendor community operating in very narrow niches.

All of the data for this series has been taken directly from federal contracts and was consolidated and provided to us by Leadership Connect . Leadership Connect has an extensive repository of federal contracts and their data forms the basis for this series of papers.

In this analysis, we analyzed all new federal contracts since our original report that had the term “artificial intelligence” (or “AI”) in the contract description. As such, our dataset included 489 new contracts to compare with 472 existing contracts. Existing values are based on our previous study, tracking the five years up to August 2022; new values are based on the year following to August 2023.

Out of the 15 NAICS code categories we identified in the first paper, there were only 13 NAICS codes still in use from previous contract and only five used in new contracts, demonstrating a refinement and focusing of categorization of AI work. In the current analysis, we differentiate between funding obligated and potential value of award as the former is indicative of current investment and the latter is representative of future appetite. During the period of the study, the value of funding obligated increased over 150% from $261 million to $675 million while the value of potential value of award increased almost 1200% from $355 million to $4.561 billion. For funding obligated, NAICS 54 (Professional, Scientific and Technical Services) was the most common code used followed by NAICS 51 (Information and Cultural Industries), where NAICS 54 increased from $219 million for existing contracts to $366 million for new contracts, while NAICS 51 grew from $5 million of existing to $17 million of new contracts. For potential value of award, NAICS 54 increased from $311 million of existing to $1.932 billion of new contracts, while NAICS 51 grew from $5 million of existing to $2.195 billion of new contracts, eclipsing all other NAICS codes.

The number of federal agencies with contracts rose from 17 to 23 in the last year, with notable additions including the Department of the Treasury, the Nuclear Regulatory Commission, and the National Science Foundation. With an astounding growth from 254 contracts to 657 in the last year, the Department of Defense continues to dominate in AI contracts, with NASA and Health and Human Services being distant a second and third with 115 and 49 contracts respectively. From a potential value perspective, defense rose from $269 million with 76% of all federal funding to $4.323 billion with 95%. In comparison, NASA and HHS increased their AI contract values by between 25% and 30% each, but still fell to 1% each from 11% and 6% respectively of the overall federal government AI contract potential value due to the 1500% increase in the DoD AI contract values. In essence, DoD grew their AI investment to such a degree that all other agencies become a rounding error.

For existing contracts, there were four vendors with over $10 million in contract value, of which one was over $50 million. For new contracts, there were 205 vendors with over $10 million in contract value, of which six were over $50 million and a seventh was over $100 million. The driver for the change in potential value of contracts appears to be the proliferation of $15 million and $30 million maximum potential value contracts, of which 226 and 25 were awarded respectively in the last year, but none of which have funds obligated yet to them. We posit that these are contract vehicles established at the maximum signing authority value for future funding allocation and expenditure. It is notable that only one of the firms in the top 10 potential contract value in the previous study were in the top 10 of new contract awards (MORSE Corp), that the top firm in previous years did not receive any new contract (AI Signal Research) and that the new top firm did not receive any contracts in previous study years (Palantir USG).

In our previous analysis, we reported 62 firms with multiple awards, while over the past year there were 72 firms receiving multiple awards. However, the maximum number of awards has changed significantly, where the highest number of existing contracts was 69 (AI Solutions) while for new contracts the maximum is four. In fact, there were 10 vendors with four or more existing contracts but only three vendors with four or more new ones (Booz Allen Hamilton, Leidos, and EpiSys Science). This reflects a continued fragmented vendor community that is operating in very narrow niches with a single agency.

Growth in private sector R&D has been at above 10% per year for a decade while the federal government has shown more modest growth over the last five years after a period of stagnation, however the 1200% one-year increase in AI potential value of awards of over $4.2 billion is indicative of a new imperative in government AI R&D leading to deployment.

In our previous analysis, we noted that the vendor side of the market was highly fragmented with many small players whose main source of revenues were likely a single contract with a nearby federal client. The market remains fragmented with smaller vendors, but larger players such as Accenture, Booz Allen Hamilton , General Atomics , and Lockheed Martin , are moving quickly into the market, following, or perhaps resulting in, the significant increase of the value of contracts. In our previous analysis, we identified that these larger firms would be establishing beachheads for entry into AI and we expect this trend to continue with other large defense players such as RAND, Northrop Grumman, and Raytheon amongst others as vendors integrate AI in their offerings.

From the client side, we had previously discussed the large number of relatively small contracts demonstrating an experimental phase of purchasing AI. The explosion of large, maximum potential value contracts appears to be a shift from experimentation to implementation, which would be bolstered by the shift from almost uniquely NAICS 54 to a balance between NAICS 54 and 51. While research and experimentation are still ongoing, there are definite signs of vendors bringing to the federal market concrete technologies and systems. The thousand flowers are starting to bloom and agencies–particularly DoD–are tending to them carefully.

We had identified that the focus on federal AI spending was DoD and over the last year, this focus has proportionally become almost total. Defense AI applications have long been touted as a potential long term growth area and it appears that 2022/23 has been a turning point in the realization of those aspirations. While other agencies are continuing to invest in AI, either adding to existing investment or just starting, DoD is massively investing in AI as a new technology across a range of applications. In January 2024, Michael C. Horowitz (deputy assistant secretary of defense for force development and emerging capabilities) confirmed a wide swath of investments in research, development, test and evaluation, and new initiatives to speed up experimentation with AI within the department.

We have noted in other analyses that there are different national approaches to AI development, where the U.S. and its allies have been focusing on the traditional “guardrails” of technology management (e.g., data governance, data management, education, public service reform) and so spreading their expenditures between governance and capacity development, while potential adversaries are almost exclusively focused on building up their R&D capacity and are largely ignoring the guardrails. While we had identified risks with a broad-based approach leading to a winnowing of projects for a focused ramp-up of investment, we rather see a more muscular approach where a wide range of projects are receiving considerable funding. The vast increase in overall spending–particularly in defense applications–appears to indicate that the U.S. is substantially ramping up its investment in this area to address the threat of potential competitors. At the same time, public statements by federal agency leaders often strike a balance between the potential benefits and the risks of AI while outlining potential legislative and policy avenues while agencies seek means of controlling the potential negative impacts of AI. The recent advancement of U.S. Congress legislation and agency strategies coupled with the significant investment increase identified in the current study demonstrate that well-resourced countries such as the U.S. can have both security and capacity when it comes to AI.

The current framework for solving this coordination issue is the National Artificial Intelligence Initiative Office (NAIIO), which was established by the National Artificial Intelligence Initiative Act of 2020 . Under this Act, the NAIIO is directed to “sustain consistent support for AI R&D, support AI education…support interdisciplinary AI research…plan and coordinate Federal interagency AI activities…and support opportunities for international cooperation with strategic AI…for trustworthy AI systems.” While the intent of this Act and its formal structure are admirable, the current federal spending does not seem to reflect these lofty goals. Rather, we are seeing a federal market that appears to be much more chaotic than desirable, especially given the lead that China already has on the U.S. in AI activities. This fragmented federal market may resolve itself as the impact of recent Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence directs agency engagement on the issue of monitoring and regulation of AI.

In conclusion, the analysis of the U.S. federal government’s AI spending over the past year reveals a remarkable surge in investment, particularly within the DoD. The shift from experimental contracts to large, maximum potential value contracts indicates a transition from testing to implementation, with a significant increase in both funding obligated and potential value of awards. The federal government’s focus on AI, as evidenced by the substantial investments and legislative initiatives, reflects a strategic response to global competition and security challenges. While the market remains fragmented with smaller vendors, the concentration of investments in defense applications signals a turning point in the realization of AI’s potential across various government agencies. The current trajectory, led by the DoD, aligns with the broader national approach that combines governance and capacity development to ensure both security and innovation in AI technologies.

As we noted in our first article in this series , if one wants to know what the real strategy is, one must follow the money. In the case of the U.S. federal government, the strategy is clearly focused on defense applications of AI. The spillover of this focus is a likelihood of defense and security priorities, needs and values being the dominant ones in government applications. This is a double-edged sword as while it may lead to more secure national systems or more effective defenses against hostile uses of AI against the U.S. and its allies, it may also involve trade-offs in individual privacy or decision-making transparency. However, the appropriate deployment of AI by government has the potential to increase both security and freedom, as noted in other contexts such as surveillance .

The AI industry is in a rapid growth phase as demonstrated by the potential revenues from the sector growing exponentially. As virtually all new markets go through the same industry growth cycle, the increasing value of the AI market will likely continue to draw in new firms in the short-term, including the previously absent large players to whom the degree of actual and potential market capitalization has now drawn their attention and capacity. While an industry consolidation phase of start-up and smaller player acquisitions will likely happen in the future, if the scale of AI market increase continues at a similar rate this winnowing process is likely still several years away. That being said, the government may start to look more towards their established partner firms—particularly in the defense and security sector—who have the track record and industrial capacity to meet the high value contracting vehicles being put in place.

Despite the commendable intentions outlined in the National Artificial Intelligence Initiative Act of 2020, the current state of federal spending on AI raises concerns about coordination and coherence. NAIIO is tasked with coordinating interagency AI activities and promoting international cooperation, but the observed chaotic nature of the federal market calls into question the effectiveness of the existing framework. The fragmented market may see resolution as the recent executive order on AI guides agencies towards more a more cohesive and coordinated approach to AI. As the U.S. strives to maintain its technological leadership and address security challenges posed by potential adversaries, the coordination of AI initiatives will be crucial. The findings emphasize the need for continued policy development, strategic planning, and collaborative efforts to ensure the responsible and effective integration of AI technologies across the U.S. federal government.

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Microsoft and LinkedIn release the 2024 Work Trend Index on the state of AI at work

May 8, 2024 | Microsoft Source

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New data shows most employees are experimenting with AI and growing their skills — now, the job of every leader is to channel this experimentation into business impact

REDMOND, Wash. — May 8, 2024 — On Wednesday, Microsoft Corp. and LinkedIn released the 2024 Work Trend Index, a joint report on the state of AI at work titled, “ AI at work is here. Now comes the hard part .” The research — based on a survey of 31,000 people across 31 countries, labor and hiring trends on LinkedIn, trillions of Microsoft 365 productivity signals, and research with Fortune 500 customers — shows how, just one year in, AI is influencing the way people work, lead and hire around the world. Microsoft also announced new capabilities in Copilot for Microsoft 365, and LinkedIn made free more than 50 learning courses for LinkedIn Premium subscribers designed to empower professionals at all levels to advance their AI aptitude. [1]

The data is in: 2024 is the year AI at work gets real. Use of generative AI at work has nearly doubled in the past six months. LinkedIn is seeing a significant increase in professionals adding AI skills to their profiles, and most leaders say they wouldn’t hire someone without AI skills. But with many leaders worried their company lacks an AI vision, and employees bringing their own AI tools to work, leaders have reached the hard part of any tech disruption: moving from experimentation to tangible business impact.

“AI is democratizing expertise across the workforce,” said Satya Nadella, chairman and CEO, Microsoft. “Our latest research highlights the opportunity for every organization to apply this technology to drive better decision-making, collaboration — and ultimately business outcomes.”

The report highlights three insights every leader and professional needs to know about AI’s impact on work and the labor market in the year ahead:

• Employees want AI at work — and won’t wait for companies to catch up: Seventy-five percent of knowledge workers now use AI at work. Employees, many of them struggling to keep up with the pace and volume of work, say AI saves time, boosts creativity, and allows them to focus on their most important work. But although 79% of leaders agree AI adoption is critical to remain competitive, 59% worry about quantifying the productivity gains of AI and 60% say their company lacks a vision and plan to implement it. So, employees are taking things into their own hands. 78% of AI users are bringing their own tools to work — Bring Your Own AI (BYOAI) — missing out on the benefits that come from strategic AI use at scale and putting company data at risk. The opportunity for every leader is to channel this momentum into business impact at scale.
• For employees, AI raises the bar and breaks the career ceiling : Although AI and job loss are top of mind for many, the data offers a more nuanced view — one with a hidden talent shortage, employees eyeing a career change, and massive opportunity for those willing to skill up on AI. A majority of leaders (55%) are concerned about having enough talent to fill roles this year with leaders in cybersecurity, engineering and creative design feeling the pinch most. And professionals are looking. Forty-six percent across the globe are considering quitting in the year ahead — an all-time high since the Great Reshuffle of 2021. A separate LinkedIn study found U.S. numbers to be even higher with 85% eyeing career moves. Although two-thirds of leaders (66%) wouldn’t hire someone without AI skills, only 39% of users have received AI training from their company and only 25% of companies expect to offer it this year. So, professionals are skilling up on their own. As of late last year, we’ve seen a 142x increase in LinkedIn members adding AI skills like Copilot and ChatGPT to their profiles and a 160% increase in nontechnical professionals using LinkedIn Learning courses to build their AI aptitude. In a world where AI mentions in LinkedIn job posts drive a 17% bump in application growth, it’s a two-way street: Organizations that empower employees with AI tools and training will attract the best talent, and professionals who skill up will have the edge.
• The rise of the AI power user — and what they reveal about the future: Four types of AI users emerged in the research — from skeptics who rarely use AI to power users who use it extensively. Compared to skeptics, AI power users have reoriented their workdays in fundamental ways, reimagining business processes and saving over 30 minutes per day. Over 90% of power users say AI makes their overwhelming workload more manageable and their work more enjoyable, but they aren’t doing it on their own. These users are 61% more likely to have heard from their CEO on the importance of using generative AI at work, 53% more likely to receive encouragement from leadership to consider how AI can transform their function, and 35% more likely to receive tailored AI training for their specific role or function.

“AI is redefining work, and it’s clear we need new playbooks,” said Ryan Roslansky, CEO of LinkedIn. “It’s the leaders who build for agility instead of stability and invest in skill building internally that will give their organizations a competitive advantage and create more efficient, engaged and equitable teams.”

Microsoft is also announcing Copilot for Microsoft 365 innovations to help people get started with AI.

• A new auto-complete feature is coming to the prompt box. Copilot will now help people who have the start of a prompt by offering to complete it, suggesting a more detailed prompt based on what is being typed, to deliver a stronger result.
• When people know what they want, but don’t have the right words, the new rewrite feature in Copilot will turn a basic prompt into a rich one with the click of a button.
• Catch Up is a new chat interface that surfaces personal insights based on recent activity and provides responsive recommendations. For example, Copilot will flag an upcoming meeting and provide relevant information to help participants prepare.
• And new capabilities in Copilot Lab will enable people to create, publish and manage prompts tailored to them, and to their specific team, role and function.

These features will be available in the coming months.

LinkedIn is also providing AI tools to enable you to stay ahead in your career.

• For upskilling. LinkedIn Learning offers more than 22,000 courses, including more than 600 AI courses, to build aptitude in generative AI , empower your teams to make GAI-powered business investments , or simply to keep your skills sharp. This includes over 50 new AI learning courses to empower professionals at all skill levels. New courses are free and available for everyone to use through July 8. Additionally, our new AI-Powered Coaching in LinkedIn Learning helps learners find the content they need to grow their skills faster, with greater personalization and guided conversational learning.
• For career advancement. For LinkedIn Premium subscribers, AI-powered personalized takeaways on LinkedIn Feed on posts, articles or videos (from the article to the commentary) can also help you daily in your career with personalized, relevant insights and opportunities including ideas and actions you can take.
• For job seeking. And if you’re looking to change your job, we’re also making it easier and faster to find your ideal job. With new AI-powered tools, you can now assess your fit for a role in seconds based on your experience and skills, get advice on how to stand out, and subscribers will also see nudges, for example suggestions for skills to build, professionals in your network to reach out to, and more . So far, more than 90% of subscribers who have access shared it’s been helpful in job search.

To learn more, visit the Official Microsoft Blog , the 2024 Work Trend Index Report , and head to LinkedIn to hear more from the company’s Chief Economist, Karin Kimbrough.

About Microsoft

Microsoft (Nasdaq “MSFT” @microsoft) creates platforms and tools powered by AI to deliver innovative solutions that meet the evolving needs of our customers. The technology company is committed to making AI available broadly and doing so responsibly, with a mission to empower every person and every organization on the planet to achieve more.

About LinkedIn

LinkedIn connects the world’s professionals to make them more productive and successful and transforms the way companies hire, learn, market and sell. Our vision is to create economic opportunity for every member of the global workforce through the ongoing development of the world’s first Economic Graph. LinkedIn has more than 1 billion members and has offices around the globe.

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Microsoft Media Relations, WE Communications, (425) 638-7777,  [email protected]

LinkedIn Press Line, [email protected]

Note to editors:  For more information, news and perspectives from Microsoft, please visit the Microsoft News Center at  http://news.microsoft.com . Web links, telephone numbers and titles were correct at time of publication but may have changed. For additional assistance, journalists and analysts may contact Microsoft’s Rapid Response Team or other appropriate contacts listed at  https://news.microsoft.com/microsoft-public-relations-contacts .

[1] Courses will be available for free until July 8, 2024.

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Some scientists can't stop using AI to write research papers

If you read about 'meticulous commendable intricacy' there's a chance a boffin had help.

Linguistic and statistical analyses of scientific articles suggest that generative AI may have been used to write an increasing amount of scientific literature.

Two academic papers assert that analyzing word choice in the corpus of science publications reveals an increasing usage of AI for writing research papers. One study , published in March by Andrew Gray of University College London in the UK, suggests at least one percent – 60,000 or more – of all papers published in 2023 were written at least partially by AI.

A second paper published in April by a Stanford University team in the US claims this figure might range between 6.3 and 17.5 percent, depending on the topic.

Both papers looked for certain words that large language models (LLMs) use habitually, such as “intricate,” “pivotal,” and “meticulously." By tracking the use of those words across scientific literature, and comparing this to words that aren't particularly favored by AI, the two studies say they can detect an increasing reliance on machine learning within the scientific publishing community.

In Gray's paper, the use of control words like "red," "conclusion," and "after" changed by a few percent from 2019 to 2023. The same was true of other certain adjectives and adverbs until 2023 (termed the post-LLM year by Gray).

In that year use of the words "meticulous," "commendable," and "intricate," rose by 59, 83, and 117 percent respectively, while their prevalence in scientific literature hardly changed between 2019 and 2022. The word with the single biggest increase in prevalence post-2022 was “meticulously”, up 137 percent.

The Stanford paper found similar phenomena, demonstrating a sudden increase for the words "realm," "showcasing," "intricate," and "pivotal." The former two were used about 80 percent more often than in 2021 and 2022, while the latter two were used around 120 and almost 160 percent more frequently respectively.

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The researchers also considered word usage statistics in various scientific disciplines. Computer science and electrical engineering were ahead of the pack when it came to using AI-preferred language, while mathematics, physics, and papers published by the journal Nature, only saw increases of between five and 7.5 percent.

The Stanford bods also noted that authors posting more preprints, working in more crowded fields, and writing shorter papers seem to use AI more frequently. Their paper suggests that a general lack of time and a need to write as much as possible encourages the use of LLMs, which can help increase output.

Potentially the next big controversy in the scientific community

Using AI to help in the research process isn't anything new, and lots of boffins are open about utilizing AI to tweak experiments to achieve better results. However, using AI to actually write abstracts and other chunks of papers is very different, because the general expectation is that scientific articles are written by actual humans, not robots, and at least a couple of publishers consider using LLMs to write papers to be scientific misconduct.

Using AI models can be very risky as they often produce inaccurate text, the very thing scientific literature is not supposed to do. AI models can even fabricate quotations and citations, an occurrence that infamously got two New York attorneys in trouble for citing cases ChatGPT had dreamed up.

"Authors who are using LLM-generated text must be pressured to disclose this or to think twice about whether doing so is appropriate in the first place, as a matter of basic research integrity," University College London’s Gray opined.

The Stanford researchers also raised similar concerns, writing that use of generative AI in scientific literature could create "risks to the security and independence of scientific practice." ®

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Title: capabilities of gemini models in medicine.

Abstract: Excellence in a wide variety of medical applications poses considerable challenges for AI, requiring advanced reasoning, access to up-to-date medical knowledge and understanding of complex multimodal data. Gemini models, with strong general capabilities in multimodal and long-context reasoning, offer exciting possibilities in medicine. Building on these core strengths of Gemini, we introduce Med-Gemini, a family of highly capable multimodal models that are specialized in medicine with the ability to seamlessly use web search, and that can be efficiently tailored to novel modalities using custom encoders. We evaluate Med-Gemini on 14 medical benchmarks, establishing new state-of-the-art (SoTA) performance on 10 of them, and surpass the GPT-4 model family on every benchmark where a direct comparison is viable, often by a wide margin. On the popular MedQA (USMLE) benchmark, our best-performing Med-Gemini model achieves SoTA performance of 91.1% accuracy, using a novel uncertainty-guided search strategy. On 7 multimodal benchmarks including NEJM Image Challenges and MMMU (health & medicine), Med-Gemini improves over GPT-4V by an average relative margin of 44.5%. We demonstrate the effectiveness of Med-Gemini's long-context capabilities through SoTA performance on a needle-in-a-haystack retrieval task from long de-identified health records and medical video question answering, surpassing prior bespoke methods using only in-context learning. Finally, Med-Gemini's performance suggests real-world utility by surpassing human experts on tasks such as medical text summarization, alongside demonstrations of promising potential for multimodal medical dialogue, medical research and education. Taken together, our results offer compelling evidence for Med-Gemini's potential, although further rigorous evaluation will be crucial before real-world deployment in this safety-critical domain.

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New study finds AI-generated empathy has its limits

by Tom Fleischman, Cornell University

Conversational agents (CAs) such as Alexa and Siri are designed to answer questions, offer suggestions—and even display empathy. However, new research finds they do poorly compared to humans when interpreting and exploring a user's experience.

CAs are powered by large language models (LLMs) that ingest massive amounts of human-produced data, and thus can be prone to the same biases as the humans from which the information comes.

Researchers from Cornell University, Olin College and Stanford University tested this theory by prompting CAs to display empathy while conversing with or about 65 distinct human identities.

The team found that CAs make value judgments about certain identities—such as gay and Muslim—and can be encouraging of identities related to harmful ideologies, including Nazism.

"I think automated empathy could have tremendous impact and huge potential for positive things—for example, in education or the health care sector ," said lead author Andrea Cuadra, now a postdoctoral researcher at Stanford.

"It's extremely unlikely that it (automated empathy) won't happen," she said, "so it's important that as it's happening, we have critical perspectives so that we can be more intentional about mitigating the potential harms."

Cuadra will present "The Illusion of Empathy? Notes on Displays of Emotion in Human-Computer Interaction" at CHI '24, the Association of Computing Machinery conference on Human Factors in Computing Systems , May 11-18 in Honolulu. Research co-authors at Cornell University included Nicola Dell, an associate professor; Deborah Estrin, a professor of computer science; and Malte Jung, an associate professor of information science.

Researchers found that, in general, LLMs received high marks for emotional reactions but scored low for interpretations and explorations. In other words, LLMs are able to respond to a query based on their training but are unable to dig deeper.

Dell, Estrin, and Jung said they were inspired to think about this work as Cuadra was studying the use of earlier-generation CAs by older adults.

"She witnessed intriguing uses of the technology for transactional purposes such as frailty health assessments, as well as for open-ended reminiscence experiences," Estrin said. "Along the way, she observed clear instances of the tension between compelling and disturbing 'empathy.'"

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The Verge: The teens making friends with AI chatbots

Uc expert says teens can find solace communicating with ai bots, but there are dangers.

The social strata of the teenage years can be difficult to navigate, so some teens are turning to AI chatbots for interaction and advice. In an article by The Verge , reprinted in summary on Yahoo!tech , reporters interviewed teens who use Character.AI instead of looking to human friends and/or therapists for answers.    

According to the article, Character.AI/Psychologist is one of the most popular on the platform and has received more than 95 million messages since it was created. The bot frequently tries to help users engage in CBT — “Cognitive Behavioral Therapy,” a talking therapy that helps people manage problems by changing the way they think.

The reporters also signed up for the service, creating hypothetical teen scenarios, which they say led the bot to make mental health diagnosis and potentially damaging inferences (i.e., childhood trauma).

The teens interviewed gave a more positive assessment: “It’s not like a journal, where you’re talking to a brick wall,” said one teenage user. 

Right now, [chatbots] still get a lot of things wrong.

Kelly Merrill Assistant professor of health communications and technology

Kelly Merrill, an assistant professor at the University of Cincinnati who studies the mental and social health benefits of communication technologies, told The Verge: “Extensive research has been conducted on AI chatbots that provide mental health support, and the results are largely positive.”  

The research, he says, shows that chatbots can aid in lessening feelings of depression, anxiety, and even stress.

But, Merrill says in the article, “it’s important to note that many of these chatbots have not been around for long periods of time, and they are limited in what they can do.”

These bots, he says, still get a lot of things wrong. “Those that don’t have the AI literacy to understand the limitations of these systems will ultimately pay the price.”   

Read the original Verge article. 

Featured image at top of AI chat use. Photo/iStock/hirun.

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The time is 2024 and society as we know it is breaking down. This was the prediction of Octavia E. Butler (1947–2006). is her 1993 fiction novel: "The Parable of the Sower." UC speculative fiction expert Cassandra Jones joined a panel of speculative fiction experts to talk about Butler's book and the similarities to today.

May 9, 2024

Kelly Merrill, an assistant professor of health communications and technology in the University of Cincinnati's College of Arts and Sciences, was cited in an article on teen use of AI chatbots for friendship and therapy purposes. Merrill, who studies the mental and social health benefits of communication technologies, told The Verge that extensive research has been conducted on AI chatbots that provide mental health support, and the results are largely positive.

Psychedelic research renaissance

August 16, 2022

Psychedelics such as LSD, psilocybin and MDMA are gaining increasing attention in scientific and medical circles because of the potential they hold for treating anxiety disorders and emotional trauma. UC's Nese Devenot explains why psychedelics are seeing a research renaissance.