Using AI to Teach Writing

Writing is one of the most important things students do at university, not because they need to produce documents, but because writing is how they develop and test their own thinking. A student who can construct a clear argument, anticipate objections, and revise their ideas in response to evidence is a student who has genuinely learned something.^{1 2} The concern with AI is not that it produces text — it is that it can produce text while completely bypassing that thinking process.

This creates both a problem and an opportunity. Students may use AI to generate submissions without engaging with the material, but a carefully designed activity can also make evaluation and revision visible. The key is deciding which parts of writing are the learning outcome and ensuring the model does not perform those parts for the student.

This article offers practical approaches for any lecturer whose course involves writing, not just those who teach writing explicitly.

Rethinking What Writing Instruction Is For

Before incorporating AI into writing tasks, it helps to be clear about what writing instruction is actually trying to achieve. If the goal is a polished, correct document, then AI poses a serious threat: it can produce such documents reliably and at scale. But if the goal is to develop a student’s capacity to think, argue, structure, and communicate — then the document is evidence of learning, not the learning itself.

This distinction changes how you design and assess writing tasks. The question to ask of every writing assignment is: what thinking does this task require, and how will I know whether the student has done it? When you can answer that question clearly, you are in a position to incorporate AI as a tool while keeping the intellectual work where it belongs — with the student.

Tasks that require students to make disciplinary judgements, apply specific course sources, defend choices, or demonstrate developing thought can provide richer evidence than a generic final essay. These features do not prove authorship, but they align assessment more closely with the intended learning.

Techniques for the Classroom

AI as First Draft

When critical evaluation is the outcome, an educator-vetted AI-generated draft can become the starting point of the assignment. Students then revise it using course knowledge, research, and disciplinary judgment. Supplying the same draft to everyone avoids requiring accounts and makes the activity easier to compare.

The submission is not the AI draft — it is the revised version, along with an annotated changelog: a document where students identify each significant change they made and explain why they made it. Did they correct a factual error? Add evidence the AI omitted? Restructure an argument that was logically weak? Replace a vague generalisation with a claim they can actually defend?

The annotation can become important evidence of learning when the rubric rewards accurate diagnosis, evidence use, and justified revision.³ It should be assessed for the quality of the decisions, not the quantity of changes.

Be explicit about what students must demonstrate. For example: require them to verify every major claim, replace unsupported reasoning, use specified course sources, and justify three consequential revisions. Avoid percentages of “student-authored” sentences, which are difficult to interpret or verify and say little about learning.

AI as Revision Partner

Where the tool and material are approved, AI feedback can be an intermediate step. Ask students to request feedback against the published criteria, then submit a short record of which suggestions they accepted or rejected and why. Do not require the full conversation unless it is necessary to the outcome.

The value of this is not that the AI’s feedback replaces yours — it often will not be as precise or disciplinary as what an experienced teacher provides — but that it creates a recorded dialogue. Students must respond to the AI’s suggestions, either by acting on them or explaining why they have not. This response is where the learning happens: a student who writes “the AI suggested I define my terms earlier, and I disagree because the argument only makes sense after the reader understands the context” is doing exactly the kind of reflective, reasoned thinking that writing instruction aims to cultivate.

Whether this saves educator time depends on the quality of the feedback and the review required. Pilot the workflow before making it part of a graded task.

Generating and Defeating Counterarguments

Argumentation is difficult to teach partly because students may not see their own arguments from the outside. AI can generate possible counterarguments for students to evaluate and answer.

Ask students to write their thesis and the main lines of their argument, then prompt an AI to argue against them as forcefully as possible. Their task is to revise their essay so that it addresses the strongest of those objections. This can be done iteratively: write, generate objections, revise, generate objections again.

The output may expose a weakness, but it can also invent an objection or misrepresent the debate. Students should compare counterarguments with course sources and decide which are legitimate before revising.

For more adversarial disciplines like law, philosophy, or policy studies, you can extend this by having the AI play the role of an opposing counsel, a philosophical interlocutor, or a policy sceptic, and asking students to sustain their argument across several rounds of challenge. This is a written version of the simulation technique and produces similar benefits in terms of deep engagement with the material.

Comparative Analysis of AI and Human Writing

Give students two texts on the same topic: one AI-generated, one written by a human expert or, for more advanced classes, two AI-generated texts produced with different prompts. Ask students to compare them analytically, using the evaluative framework from your discipline.

What makes one more persuasive than the other? Which handles evidence better? Where does one simplify something the other engages with carefully? What is present in the human-authored text that the AI version lacks, and vice versa? This comparative exercise builds students’ sense of quality in your discipline — their understanding of what a genuinely good piece of writing in this field looks like — which is foundational for their own writing development.

For writing teachers, this can become an exercise in voice, specificity, and structure. Avoid assuming that all model output is generic or all human writing is distinctive; ask students to identify evidence for each judgment.

Structured Brainstorming

AI can support structured brainstorming by proposing angles or questions. Students must verify any claimed debate or source and make the substantive choice themselves.

The task is then to make a deliberate choice: which of these directions do they actually find most interesting or defensible, and why? Requiring students to document their brainstorming session and reflect on the choices they made surfaces the intellectual judgement that is often invisible in a polished final submission.

This may help some students begin, but it can also anchor them to the model’s framing. Ask students to generate ideas independently first or compare the AI list with a course-grounded map of the field.

Targeted Language Feedback for Multilingual Students

For students writing in an additional language, surface-level issues can obscure their understanding. Approved AI tools may offer grammar, syntax, or clarity feedback, but quality varies by language and variety and suggestions can erase a student’s intended voice.

Make this support optional unless the institution provides an accessible, approved tool. Teach students to review every edit and preserve meaning rather than mechanically accepting suggestions.

It is worth being explicit with students about this division of labour, and about the fact that surface fluency matters in professional contexts — but that it is separate from the intellectual quality of their work, which is what your assessment primarily concerns.

Designing Writing Assignments That Work With AI

The techniques above work best when the underlying assignment is designed with AI in mind from the start. A few principles:

Tie the task to course learning. An assignment that asks students to “discuss climate policy” elicits little disciplinary evidence. Asking them to evaluate a specific policy using a framework taught in the course and supplied sources makes the intended reasoning clearer. This improves alignment; it does not make the task immune to outsourcing.

Collect proportionate process evidence. Drafts, selected notes, or annotated changes can support feedback and reflection. Request only what the outcome and rubric require; full transcripts, screenshots, and writing histories can collect personal or irrelevant information and do not prove authorship.

Ask for genuine personal judgement. Prompts that require students to take and defend a position — including one that could be contested by a reasonable person — force a kind of commitment that AI-generated text typically avoids. “Evaluate the argument made by [specific scholar] in [specific text]” is much harder to outsource than “discuss the strengths and weaknesses of social media.”

Use reflection for learning, not authentication. Questions such as “Which claim changed most during revision, and what evidence caused the change?” can make decisions visible. Treat the response as assessable reflection, not proof that no outside assistance was used.

Integrating AI Writing Tools Into Your Course Policy

If you plan to use any of these techniques, it is worth being explicit with students about your course’s approach to AI from the outset, rather than leaving them to guess. Clarity about when and how AI may be used — and what counts as the student’s own contribution — reduces anxiety, reduces unintentional misuse, and creates a better learning environment than ambiguity does.

Distinguish between phases of the writing process: AI might be allowed for brainstorming but not submitted prose, or allowed for feedback with a short record of consequential suggestions. AI-free writing is also appropriate when independent writing is the outcome. Use the templates in Setting course and assignment AI rules.

Whatever your policy, the most important thing is to pair it with explicit instruction in how to evaluate what AI produces. Students who understand AI’s limitations — its tendency to hallucinate sources, flatten complexity, and produce confident-sounding errors — are better placed to use it responsibly and to recognise when its outputs are not good enough to build on.

Example Prompts

The two prompts below illustrate different roles for AI in the writing process. Use them only with material approved for the selected tool, and adapt them to the published assignment criteria.

For revision feedback

For generating counterarguments

In both cases, remind students that the AI’s feedback is a prompt for their own thinking, not a set of instructions to follow mechanically. Part of the intellectual work is deciding which of the AI’s observations are worth acting on, which are not, and why — a judgement that only the student, with their own understanding of the material, is in a position to make.

References & Footnotes