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UK University Essay Sample | Brief-Based 2,000 Words

Assessment Brief: Academic Essay

Module Title: Academic Writing and Critical Thinking
Assessment Type: Individual Essay
Word Count: 2,000 words
Weighting: 60%
Submission Format: Word document or PDF
Referencing Style: Harvard referencing

Essay Question

To what extent is AI feedback changing the way university students write essays?

You should discuss whether AI tools are helping students improve their academic writing, or whether they are making students too dependent on instant feedback. Your answer must include clear arguments, examples, and academic sources.

What You Need to Do

You are required to write a critical essay that explores how AI feedback tools are being used by university students during essay writing. You may discuss tools that check grammar, suggest structure, improve clarity, or give instant comments on written work.

Your essay should not simply describe AI tools. You must build an argument and show your own judgement.

Your Essay Should Include

• A clear introduction with your main argument
• Discussion of how students use AI feedback when writing essays
• Benefits of AI feedback, such as grammar support, clarity, and confidence
• Risks, such as over-reliance, weak independent thinking, and loss of student voice
• Academic sources to support your points
• A balanced conclusion that answers the essay question directly

Learning Outcomes Assessed

By completing this assessment, you should be able to:

1. Write a clear academic essay with a logical argument.
2. Use academic sources to support your discussion.
3. Show critical thinking instead of only describing the topic.
4. Apply Harvard referencing correctly.
5. Present a balanced answer to a current issue in higher education.

Marking Criteria

Argument and critical thinking – 30%
Your essay should have a clear line of argument and should not read like a list of points.

Use of sources – 25%
You should use relevant academic sources and link them properly to your discussion.

Essay structure – 20%
Your essay should have a clear introduction, well-developed paragraphs, and a direct conclusion.

Academic writing style – 15%
Your writing should be formal, clear, and suitable for university work.

Referencing and presentation – 10%
Harvard referencing must be accurate, and the work should be neatly presented.

Additional Guidance

Avoid writing a general essay about technology. Stay focused on essay writing and student learning. Strong essays will explain how AI feedback affects the writing process, not just whether AI is “good” or “bad”.

You should also avoid using personal opinion only. Your answer must be supported with academic reading, examples, and clear reasoning.

Suggested Starting Points

You may consider the following ideas:

• Does AI feedback help students understand their mistakes?
• Can students still develop their own academic voice when using AI tools?
• Should universities teach students how to use AI feedback properly?
• Is AI feedback different from tutor feedback?
• Does instant correction improve learning, or does it make students avoid deeper thinking?

Submission Instructions

Submit your essay through the online submission portal before the deadline. Late submissions may be penalised according to university policy. Your work must be original and properly referenced.

To what extent is AI feedback changing the way university students write essays?

AI feedback is changing university essay writing, but not in a simple “good” or “bad” way. It is not only a new grammar checker sitting at the end of the writing process. For many students, it now appears much earlier: when they are trying to understand the question, improve a paragraph, check whether an argument is clear, or make the writing sound more academic. This matters because feedback has always shaped how students learn to write. Hattie and Timperley (2007) argue that feedback can have a strong effect on learning, but only when it helps the learner understand where they are, where they need to go, and what they should do next. AI feedback can do some of this quickly, especially with language and clarity. However, it can also make students accept easy corrections without thinking properly about their own argument. This essay argues that AI feedback is changing essay writing to a large extent at the drafting and editing stages, but it should not be treated as a replacement for student judgement, tutor feedback, or subject knowledge.

One clear change is speed. In the past, students often waited days or weeks for feedback from a tutor, or they relied on a friend to read their work. AI tools can now comment almost instantly on grammar, sentence length, paragraph flow and sometimes the strength of an argument. This is useful because writing is not a one-time action. Students usually write, read, change, delete and rewrite. Sadler (1989) explains that good formative feedback should help students judge the quality of their own work and know how to improve it. When used carefully, AI feedback can support this process because a student can test a paragraph, see what is unclear, and then revise it before submission. For a student who struggles with academic English, even a small comment such as “this sentence is too vague” can make the writing process less stressful. It gives the student a chance to fix the problem while the essay is still being built.

AI feedback can also help students notice problems that they may miss in their own writing. Many essays lose marks not because the student has no ideas, but because the ideas are hidden under unclear sentences or weak paragraph movement. Hyland and Hyland (2006) note that feedback is important in writing development because it supports students as they gain more control over composing. AI tools can point out repeated wording, overlong sentences, missing signposting, or places where a claim needs evidence. These are not small issues. In essay writing, unclear expression can make a good argument look weak. If a student writes, “technology is useful in education”, AI feedback may push them to say what type of technology, for whom, and in what situation. That kind of prompt can move the student from a general sentence to a more precise academic point.

However, the usefulness of AI feedback depends on how the student responds to it. Nicol and Macfarlane-Dick (2006) argue that good feedback should help students become more self-regulated learners, not just passive receivers of correction. This is where AI feedback can become risky. If students accept every suggestion because it sounds confident, they may stop asking whether the change is actually right for the essay question. A tool might make a paragraph sound smoother, but smoother does not always mean better. A paragraph can be polished and still fail to answer the task. This is a serious issue in university writing because marks are not given for neat language alone. They are given for argument, evidence, analysis and relevance to the brief.

A second important change is confidence. Many students feel embarrassed when their writing is marked heavily with corrections. They may read the comments once, feel discouraged, and then avoid looking at them again. Carless and Boud (2018) describe feedback literacy as the ability to understand feedback, make judgements and take action from it. AI feedback may help some students build this confidence because it feels private and low-pressure. A student can ask for help with a rough paragraph without feeling that a tutor is judging them. They can try several versions before showing the work to anyone else. This can be especially helpful for first-year students, international students, or students returning to study after a long break, because they can practise academic writing without waiting for formal feedback every time.

Still, confidence can turn into dependence. If a student begins to ask a tool to improve every sentence, the final essay may no longer sound like the student’s own work. It may become clean but flat. Black and Tomlinson (2025) found that students used AI in different ways, including lower-order writing tasks such as editing and proofreading, but also more complex tasks such as finding evidence and developing ideas. This shows why the issue is not only grammar. Once AI feedback starts shaping the content of the essay, it becomes harder to separate support from substitution. If a tool suggests the argument, selects the evidence and rewrites the wording, the student may have submitted a text they can barely explain. In that situation, the student has not really learned how to write a stronger essay. They have only learned how to accept a better-sounding version.

This problem links closely to academic integrity. UK universities are not simply banning AI in all situations, but they are trying to make its use clearer. The Russell Group (2023) states that students and staff should become AI-literate, while universities should also protect academic rigour and integrity. This is a sensible position because students will not stop using these tools. A total ban may push use underground, while no guidance at all leaves students guessing. The fairer approach is to teach students what kind of AI feedback is acceptable. For example, asking for grammar advice or a comment on whether a paragraph is clear may be very different from asking a tool to write the paragraph. The problem is that students do not always understand this difference unless it is explained properly.

AI feedback is also changing the role of tutor feedback. It does not remove the need for tutors, but it may change what students expect from them. If AI can fix spelling and basic grammar, students may expect tutors to focus more on the argument, reading, method and subject-specific judgement. This could be positive. Tutors often spend time correcting surface-level issues when they would rather comment on deeper problems. However, AI feedback cannot fully understand a module’s marking style, the tutor’s expectations, or the small details of a specific assignment brief. It may give advice that sounds useful but does not match the task. For instance, it may tell a student to add more background when the essay already has too much description and not enough evaluation. This is why students still need human feedback from someone who understands the course, the level and the marking criteria.

This is also where the difference between instant feedback and good feedback becomes important. Hattie and Timperley (2007) warn that feedback is not automatically effective; its effect depends on the type of information given and how it is used. AI feedback can be fast, but speed alone is not the same as learning. A student can receive ten suggestions in ten seconds and still not understand why the paragraph was weak. In some cases, quick feedback may even encourage shallow revision. The student changes words, accepts a new sentence, and feels the essay has improved. Yet the deeper issue may remain: the paragraph does not make a point, the evidence is not analysed, or the conclusion does not answer the question. University essays require more than correction. They require students to make choices, defend those choices and show why their answer is convincing.

Another concern is that AI feedback may make student writing more similar. Essay writing is not meant to be casual speech, but it should still show the student’s own thinking. If many students use the same tools and accept the same kind of suggestions, their essays may start to share the same safe phrases and predictable structure. This matters because academic writing is not only about correctness. It is also about judgement: deciding which point matters, which source is stronger, where to disagree, and how to answer the exact wording of the question. A tool may improve the surface of the essay, but it cannot take responsibility for the student’s judgement. If every paragraph becomes too smooth, too balanced and too cautious, the writing may lose the sharpness that comes from a student actually taking a position.

At the same time, it would be unfair to ignore the benefits. AI feedback can make writing more accessible. Students with weaker grammar, limited confidence, or less experience of academic writing can use it as a first reader. It can help them spot unclear wording before a tutor sees the work. It can also encourage more revision, because feedback is available during the writing process rather than only after submission. This links with formative assessment theory, where improvement happens while the work is still being produced, not only after it has been graded (Sadler, 1989). In this sense, AI feedback can support learning if it is used as a prompt for revision rather than as a replacement for thinking. It may also help students ask better questions when they do meet a tutor, because they have already dealt with some of the basic writing issues.

There is also an access issue that should not be ignored. Some students may already have strong family support, private tutors, or friends who understand academic writing. Others may be working long hours, studying in a second language, or trying to understand university expectations for the first time. AI feedback can reduce this gap a little because it gives students a place to test their writing before submission. However, access is still uneven. Some tools are free but limited, while stronger versions may sit behind paid subscriptions. The Russell Group (2023) recognises that universities need to think about fairness of access as AI becomes part of learning. If one student can use a better tool than another, feedback support itself may become unequal. This is why universities should not simply tell students to “use AI wisely”. They need clear rules, examples of acceptable use, and fair access to approved tools where possible.

The strongest use of AI feedback is probably as a checking tool, not as a thinking tool. A student might write a paragraph first, then ask whether the point is clear, whether the topic sentence matches the evidence, or whether the conclusion answers the question. The student should then decide what to keep, reject or change. This keeps ownership with the writer. It also fits the idea of feedback literacy, because the student is not just receiving comments but making decisions about them (Carless and Boud, 2018). Universities should therefore teach students to question AI feedback in the same way they question sources. Does the comment fit the brief? Does it improve the argument? Has it changed the meaning? Would the student be able to explain the final paragraph in their own words? These questions matter because AI feedback is useful only when the student remains in control of the essay.

In conclusion, AI feedback is changing the way university students write essays to a considerable extent, especially in drafting, editing and self-checking. It gives students quicker access to comments, helps them notice unclear writing, and can make the revision process less intimidating. These are real benefits, particularly when feedback from tutors is limited or delayed. However, AI feedback also creates risks. It can encourage dependence, weaken student voice, give task-mismatched advice, and make students think that polished language is the same as critical argument. The best answer is not to reject AI feedback completely, but to use it with clear limits. AI feedback can be a useful second reader, but the student still has to do the harder work: understanding the question, choosing the evidence, building the argument and making the final judgement.

References

Black, R.W. and Tomlinson, B. (2025) ‘University students describe how they adopt AI for writing and research in a general education course’, Scientific Reports, 15, Article 8799.

Carless, D. and Boud, D. (2018) ‘The development of student feedback literacy: enabling uptake of feedback’, Assessment & Evaluation in Higher Education, 43(8), pp. 1315–1325.

Hattie, J. and Timperley, H. (2007) ‘The power of feedback’, Review of Educational Research, 77(1), pp. 81–112.

Hyland, K. and Hyland, F. (2006) ‘Feedback on second language students’ writing’, Language Teaching, 39(2), pp. 83–101.

Nicol, D.J. and Macfarlane-Dick, D. (2006) ‘Formative assessment and self-regulated learning: a model and seven principles of good feedback practice’, Studies in Higher Education, 31(2), pp. 199–218.

QAA (n.d.) QAA advice and resources on Generative AI. Accessed: 10 June 2026.

Russell Group (2023) Principles on the use of generative AI tools in education. Accessed: 10 June 2026.

Sadler, D.R. (1989) ‘Formative assessment and the design of instructional systems’, Instructional Science, 18, pp. 119–144

This sample shows how a UK university essay brief can be turned into a complete academic answer with a clear argument, Harvard referencing and a direct conclusion. If you need help with a similar essay, visit our essay writing service UK page.