a level biology essay examples

• April 25, 2023
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Mastering A Level Biology Essays: Smart Tips and Unbeatable Examples

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Introduction

A Level Biology is a challenging but rewarding course that covers a wide range of topics, from DNA and genetic inheritance to ecosystems and biodiversity. The key to success in this subject lies in understanding and applying the core principles of Biology and expressing your understanding in well-structured, coherent essays. In this article, we will provide you with some essential tips for writing outstanding A Level Biology essays, as well as presenting clear examples to help you master the essay-writing process.

• Understand the essay question

The first and most important step in writing an A Level Biology essay is to clearly understand the question. Break down the question into its key terms and implications, and ensure you comprehend what the examiner is asking you to discuss. Make a note of any key words or phrases that should feature in your essay, as these will help you structure your response and ensure you cover all the necessary points.

• Plan your essay

Before you begin writing your essay, take the time to plan your response. Create an outline that maps out the main points you want to make, as well as the order in which you will discuss them. This will enable you to develop a logical and coherent argument that addresses all the key aspects of the question.

• Include an engaging introduction

An effective introduction is crucial to grabbing the reader’s attention and setting the tone for your essay. Begin with a general statement that links to the essay question, and then narrow down your focus to present your main argument or line of inquiry. Finish your introduction with a clear thesis statement, which outlines the central points you will cover in your essay, demonstrating a solid understanding of the topic.

Example: The discovery of DNA and the subsequent advancements in genetic research have proven instrumental in understanding the role of genetics ininheritance of traits and diseases. This essay will discuss the role of genetic inheritance in the development of several human diseases, namely: Cystic Fibrosis, Huntington’s disease, and Alzheimer’s disease, as well as the ethical implications surrounding genetic testing and treatment.

• Use specific examples to support your arguments

In A Level Biology essays, it is essential to provide examples that demonstrate your understanding of the material and support your claims. Try to include a range of examples from different areas of the subject to show that you have a comprehensive and in-depth understanding of the course material.

Example: Cystic Fibrosis is an example of a genetic disorder caused by a mutation in the CFTR gene, which results in thick and sticky mucus production in affected individuals. This condition can lead to respiratory and digestive complications, illustrating the significant impact of genetic inheritance on an individual’s health.

• Synthesize information from multiple sources

To demonstrate a high level of understanding, A Level Biology essays should integrate information from various sources, such as class notes, textbooks, and scientific articles. Be sure to support your ideas with specific references to the source material, and use your own words to explain the concepts in a clear and concise manner.

• Address counterarguments and controversies

In any scientific field, there are often debates and controversies surrounding key concepts and theories. To show a comprehensive grasp of the subject matter, be sure to address counterarguments and discuss opposing viewpoints in your essay.

Example: While genetic testing for diseases such as Huntington’s has the potential to provide valuable information for individuals at risk, there are ethical concerns about the potential misuse of genetic information by employers, insurance companies, and even government entities. Weighing the benefits of genetic testing and treatment against these ethical concerns is an ongoing debate within the scientific community.

• Write a strong conclusion

To wrap up your essay, restate your main argument and summarize the key points you have made. Provide a clear and concise conclusion that demonstrates the significance of your argument and its implications for the broader field of Biology.

Example: In conclusion, the role of genetic inheritance in human diseases, as illustrated by Cystic Fibrosis, Huntington’s disease, and Alzheimer’s disease, underscores the immense potential of genetic research to improve our understanding of human health. However, as we continue to advance our knowledge and develop new treatments and testing methods, it is crucial that we remain conscious of the ethical implications that come with such advancements in order to protect individuals’ rights and liberties.

• Proofread and edit your essay

Finally, make sure you thoroughly proofread and edit your essay to correct any grammar, spelling, or punctuation errors, and to ensure that your argument flows smoothly and logically. Consider asking a friend or peer to review your essay and provide feedback – a fresh perspective can help you identify areas for improvement that you may have overlooked.

In summary, mastering A Level Biology essays involves understanding the essay question, planning a clear and logical response, using specific examples and evidence, synthesizing information from multiple sources, addressing counterarguments and controversies, and crafting a compelling introduction and conclusion. By following these steps and using the examples provided, you will be well on your way to delivering high-quality, insightful essays that demonstrate an excellent understanding of the complex and fascinating world of Biology.

Good luck, and happy essay writing!

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How to answer A-level Biology essay questions

The Synoptic essay questions in paper 3 of the AQA A-level biology course carries 25 marks, so can make a big difference to your overall grade. The essay encourages you to think across different topics (some call this “synoptic” or “holistic” thinking). This is also valuable for other areas of the exams, particularly the application questions .

Here are a few tips for tackling your A-level biology essay: (As an example, Let’s use the title “The importance of movement in cells and tissues”)

Think broadly…

Essay titles are deliberately vague to give you the chance to show your knowledge in a variety of topics. To help you to choose what’s most relevant, look out for subjective words like “movement” and “cells”. Think of the possible alternatives. “Cells”, for example, hints that you could discuss plants, animals, single-celled organisms etc. in your essay. “Movement” could mean short distances (across a membrane) or much further (circulation or mass flow) or even the whole tissue moving (phototropism).

Visualize and catastrophize!

If you can’t think of enough topics for an “importance” essay, flip the question around – what would happen if movement in cells suddenly ground to a halt? Or if ATP suddenly vanished, or our cells were robbed of their ions? Sometimes catastrophizing in this way frees up thoughts, and helps you see what is really important. Another way is to picture every diagram in your notes or textbook that features the thing-that’s-important-in-the-question. If it’s in a diagram, it could go in your essay.

Pick your strongest topics

It’s possible to drop marks on the essay for scientific errors. With that in mind, even if you have lots of ideas for things to include, always pick the topics you can write confidently about. For bonus marks, try to order them in a way that “flows” like a story. Take care to use A-level (and not GCSE) language too.

Keep an eye on the question

While you’re writing, keep asking yourself “Where am I placing the emphasis?”. If the essay is about movement, perhaps you don’t need a whole page about nervous responses, only the bits where movement is involved – sodium ions flowing through channels, waves of depolarisation etc. Movement is the focus of the essay, after all, not neurons. Always ask yourself “Does this sentence fit with the title of the essay?”

Use outside knowledge

The top marks for the essay are reserved for displaying “evidence of reading beyond specification requirements”. Often you can drop recent scientific research or news into your writing – how vaccines tackle COVID might work in an essay about viruses, for example. I often encourage students to have a quick look at the news pages of New Scientist pages (eg https://www.newscientist.com/subject/health/ ) before paper 3 for inspiration – you don’t need to spend ages doing this, just long enough to have one or two ideas up your sleeve…

Good luck with your essay!

If you’d like to work through some example essay questions, please get in touch with me at Woolton Tutors (or Woolton Biology) and we can set up some online biology tutoring sessions.

Best wishes,

Dr John Ankers

Specialist online A-level biology tutor

www.wooltontutors.co.uk

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Dr John Ankers is a tutor, coach and writer. For writing and consultancy work, please contact me at [email protected] View all posts by Dr John Ankers

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Top mark A-Level Biology Essays

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Top Mark A-Level Biology essay - The importance of DNA in science and technology

Top Mark A-Level Biology essay - The membranes of different types of cells & their functions

Full Mark A-Level Biology essay - How is energy transferred within and between organisms

Top Marks A-Level Biology essay - The Importance of Movement in Living Organisms

Top mark A-Level Biology Essay - Different types of relationships and interactions between organisms

Full Marks A-Level Biology Essay - Carbon dioxide may affect organisms directly or indirectly.

Top marks A-Level Biology Essay - Explain the importance of shapes fitting together

A collection of high mark, sample essays for A-Level Biology, providing helpful revision content and examples of logical structuring.

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• The Importance of movement in living organisms
• Explain the importance of shapes fitting together in cells and organisms
• Carbon dioxide may affect organisms directly or indirectly.
• How is energy transferred within and between organisms
• Different types of relationships and interactions between organisms
• The membranes of different types of cells are involved in many different functions
• The importance of DNA in science and technology

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I am sorry but do not waste your money on this. It is not full or top mark essays, it is very clearly taken a lot of time to think and write it through, which is not how essay writing will be under exam conditions. It is super wordy, not enough depth in the points made. I got 2 of my teachers and including my A* friends to review a few of the essays and no one put this even in the Relational section of the mark scheme. It is falsely advertised.

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Really good, thank you so much! Will be a useful revision tool too. Would be even better if we could see how the essays were marked/ examiner feedback.

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What is generative AI?

In the months and years since ChatGPT burst on the scene in November 2022, generative AI (gen AI) has come a long way. Every month sees the launch of new tools, rules, or iterative technological advancements. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good. In the years since its wide deployment, machine learning has demonstrated impact in a number of industries, accomplishing things like medical imaging analysis  and high-resolution weather forecasts. A 2022 McKinsey survey shows that AI adoption has more than doubled  over the past five years, and investment in AI is increasing apace. It’s clear that generative AI tools like ChatGPT (the GPT stands for generative pretrained transformer) and image generator DALL-E (its name a mashup of the surrealist artist Salvador Dalí and the lovable Pixar robot WALL-E) have the potential to change how a range of jobs are performed. The full scope of that impact, though, is still unknown—as are the risks.

Get to know and directly engage with McKinsey's senior experts on generative AI

Aamer Baig is a senior partner in McKinsey’s Chicago office;  Lareina Yee  is a senior partner in the Bay Area office; and senior partners  Alex Singla  and Alexander Sukharevsky , global leaders of QuantumBlack, AI by McKinsey, are based in the Chicago and London offices, respectively.

Still, organizations of all stripes have raced to incorporate gen AI tools into their business models, looking to capture a piece of a sizable prize. McKinsey research indicates that gen AI applications stand to add up to $4.4 trillion  to the global economy—annually. Indeed, it seems possible that within the next three years, anything in the technology, media, and telecommunications space not connected to AI will be considered obsolete or ineffective .

But before all that value can be raked in, we need to get a few things straight: What is gen AI, how was it developed, and what does it mean for people and organizations? Read on to get the download.

To stay up to date on this critical topic, sign up for email alerts on “artificial intelligence” here .

Learn more about QuantumBlack , AI by McKinsey.

What every CEO should know about generative AI

What’s the difference between machine learning and artificial intelligence, about quantumblack, ai by mckinsey.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Artificial intelligence is pretty much just what it sounds like—the practice of getting machines to mimic human intelligence to perform tasks. You’ve probably interacted with AI even if you don’t realize it—voice assistants like Siri and Alexa are founded on AI technology, as are customer service chatbots that pop up to help you navigate websites.

Machine learning is a type of artificial intelligence. Through machine learning, practitioners develop artificial intelligence through models that can “learn” from data patterns without human direction. The unmanageably huge volume and complexity of data (unmanageable by humans, anyway) that is now being generated has increased machine learning’s potential , as well as the need for it.

What are the main types of machine learning models?

Machine learning is founded on a number of building blocks, starting with classical statistical techniques  developed between the 18th and 20th centuries for small data sets. In the 1930s and 1940s, the pioneers of computing—including theoretical mathematician Alan Turing—began working on the basic techniques for machine learning. But these techniques were limited to laboratories until the late 1970s, when scientists first developed computers powerful enough to mount them.

Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Generative AI was a breakthrough. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.

Introducing McKinsey Explainers : Direct answers to complex questions

How do text-based machine learning models work how are they trained.

ChatGPT may be getting all the headlines now, but it’s not the first text-based machine learning model to make a splash. OpenAI’s GPT-3 and Google’s BERT both launched in recent years to some fanfare. But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner .

The first machine learning models to work with text were trained by humans to classify various inputs according to labels set by researchers. One example would be a model trained to label social media  posts as either positive or negative. This type of training is known as supervised learning because a human is in charge of “teaching” the model what to do.

The next generation of text-based machine learning models rely on what’s known as self-supervised learning. This type of training involves feeding a model a massive amount of text so it becomes able to generate predictions. For example, some models can predict, based on a few words, how a sentence will end. With the right amount of sample text—say, a broad swath of the internet—these text models become quite accurate. We’re seeing just how accurate with the success of tools like ChatGPT.

What does it take to build a generative AI model?

Building a generative AI model has for the most part been a major undertaking, to the extent that only a few well-resourced tech heavyweights have made an attempt . OpenAI, the company behind ChatGPT, former GPT models, and DALL-E, has billions in funding from bold-face-name donors. DeepMind is a subsidiary of Alphabet, the parent company of Google, and even Meta has dipped a toe into the generative AI model pool with its Make-A-Video product. These companies employ some of the world’s best computer scientists and engineers.

But it’s not just talent. When you’re asking a model to train using nearly the entire internet, it’s going to cost you. OpenAI hasn’t released exact costs, but estimates indicate that GPT-3 was trained on around 45 terabytes of text data—that’s about one million feet of bookshelf space, or a quarter of the entire Library of Congress—at an estimated cost of several million dollars. These aren’t resources your garden-variety start-up can access.

What kinds of output can a generative AI model produce?

As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny. The results depend on the quality of the model—as we’ve seen, ChatGPT’s outputs so far appear superior to those of its predecessors—and the match between the model and the use case, or input.

ChatGPT can produce what one commentator called a “ solid A- ” essay comparing theories of nationalism from Benedict Anderson and Ernest Gellner—in ten seconds. It also produced an already famous passage describing how to remove a peanut butter sandwich from a VCR in the style of the King James Bible. Image-generating AI models like DALL-E 2 can create strange, beautiful images on demand, like a Raphael painting of a Madonna and child, eating pizza . Other generative AI models can produce code, video, audio, or business simulations .

But the outputs aren’t always accurate—or appropriate. When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly.

Generative AI outputs are carefully calibrated combinations of the data used to train the algorithms. Because the amount of data used to train these algorithms is so incredibly massive—as noted, GPT-3 was trained on 45 terabytes of text data—the models can appear to be “creative” when producing outputs. What’s more, the models usually have random elements, which means they can produce a variety of outputs from one input request—making them seem even more lifelike.

What kinds of problems can a generative AI model solve?

The opportunity for businesses is clear. Generative AI tools can produce a wide variety of credible writing in seconds, then respond to criticism to make the writing more fit for purpose. This has implications for a wide variety of industries, from IT and software organizations that can benefit from the instantaneous, largely correct code generated by AI models to organizations in need of marketing copy. In short, any organization that needs to produce clear written materials potentially stands to benefit. Organizations can also use generative AI to create more technical materials, such as higher-resolution versions of medical images. And with the time and resources saved here, organizations can pursue new business opportunities and the chance to create more value.

We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies. Companies looking to put generative AI to work have the option to either use generative AI out of the box or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines.

What are the limitations of AI models? How can these potentially be overcome?

Because they are so new, we have yet to see the long tail effect of generative AI models. This means there are some inherent risks  involved in using them—some known and some unknown.

The outputs generative AI models produce may often sound extremely convincing. This is by design. But sometimes the information they generate is just plain wrong. Worse, sometimes it’s biased (because it’s built on the gender, racial, and myriad other biases of the internet and society more generally) and can be manipulated to enable unethical or criminal activity. For example, ChatGPT won’t give you instructions on how to hotwire a car, but if you say you need to hotwire a car to save a baby, the algorithm is happy to comply. Organizations that rely on generative AI models should reckon with reputational and legal risks involved in unintentionally publishing biased, offensive, or copyrighted content.

These risks can be mitigated, however, in a few ways. For one, it’s crucial to carefully select the initial data used to train these models to avoid including toxic or biased content. Next, rather than employing an off-the-shelf generative AI model, organizations could consider using smaller, specialized models. Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases. Organizations should also keep a human in the loop (that is, to make sure a real human checks the output of a generative AI model before it is published or used) and avoid using generative AI models for critical decisions, such as those involving significant resources or human welfare.

It can’t be emphasized enough that this is a new field. The landscape of risks and opportunities  is likely to change rapidly in coming weeks, months, and years. New use cases are being tested monthly, and new models are likely to be developed in the coming years. As generative AI becomes increasingly, and seamlessly, incorporated into business, society, and our personal lives, we can also expect a new regulatory climate  to take shape. As organizations begin experimenting—and creating value—with these tools, leaders will do well to keep a finger on the pulse of regulation and risk.

Articles referenced include:

• " Implementing generative AI with speed and safety ,” March 13, 2024, Oliver Bevan, Michael Chui , Ida Kristensen , Brittany Presten, and Lareina Yee
• “ Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom ,” February 22, 2024, Venkat Atluri , Peter Dahlström , Brendan Gaffey , Víctor García de la Torre, Noshir Kaka , Tomás Lajous , Alex Singla , Alex Sukharevsky , Andrea Travasoni , and Benjamim Vieira
• “ As gen AI advances, regulators—and risk functions—rush to keep pace ,” December 21, 2023, Andreas Kremer, Angela Luget, Daniel Mikkelsen , Henning Soller , Malin Strandell-Jansson, and Sheila Zingg
• “ The economic potential of generative AI: The next productivity frontier ,” June 14, 2023, Michael Chui , Eric Hazan , Roger Roberts , Alex Singla , Kate Smaje , Alex Sukharevsky , Lareina Yee , and Rodney Zemmel
• “ What every CEO should know about generative AI ,” May 12, 2023, Michael Chui , Roger Roberts , Tanya Rodchenko, Alex Singla , Alex Sukharevsky , Lareina Yee , and Delphine Zurkiya
• “ Exploring opportunities in the generative AI value chain ,” April 26, 2023, Tobias Härlin, Gardar Björnsson Rova , Alex Singla , Oleg Sokolov, and Alex Sukharevsky
• “ The state of AI in 2022—and a half decade in review ,” December 6, 2022,  Michael Chui ,  Bryce Hall ,  Helen Mayhew , Alex Singla , and Alex Sukharevsky
• “ McKinsey Technology Trends Outlook 2023 ,” July 20, 2023,  Michael Chui , Mena Issler,  Roger Roberts , and  Lareina Yee  
• “ An executive’s guide to AI ,” Michael Chui , Vishnu Kamalnath, and Brian McCarthy
• “ What AI can and can’t do (yet) for your business ,” January 11, 2018,  Michael Chui , James Manyika , and Mehdi Miremadi

This article was updated in April 2024; it was originally published in January 2023.

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