Written by: Alessandra Giugliano

Generative AI has become an everyday tool for graduate students; surveys show that AI use in UK higher education jumped from 66 % to 92 % in a single year, and roughly 88 % of students employ AI tools in their academic work. Attempts to ban AI outright often fail because detection tools are unreliable and prone to false positives, and the punitive approach undermines trust. 

Instead of searching for mythical “AI‑proof” assignments, graduate instructors can design assessments that integrate AI responsibly. This post presents eight assignment patterns that require students to work with AI and to exercise human judgement, critical thinking and ethical reasoning. We also explain how to scaffold these tasks, safeguard academic integrity and adapt them to different disciplines.

The Limits of “No AI” Policies

The urge to ban AI often stems from concerns about plagiarism and cognitive offloading. However, generative models can already produce fluent responses that evade detection, and new models emerge faster than detection tools can keep up. High‑risk assignments, such as take‑home essays and problem sets, are easily handled by AI. 

Without guidance, bans can widen equity gaps; students with prior access to AI will remain proficient while others miss out on learning essential tools. Rather than policing, instructors should aim to make the learning process transparent and assess the skills that AI cannot provide: reasoning, critique, synthesis, and communication.

Design Principles for AI‑Inclusive Assessments

Across the research literature on AI in higher education, several principles stand out when it comes to “AI-proofing” assignments or learning how to adapt assignments to current AI use:

1. Clarify learning outcomes: 

   • Start by defining what students should know or be able to do by the end of the assignment.

   • Align tasks with these outcomes and avoid prompts that ask for generic summaries that AI can easily produce.

2. Scaffold assignments: 

   • Breaking large projects into in‑person proposals, outlines, literature reviews, and oral check‑ins helps monitor progress and reduces the temptation to outsource the entire task. 

   • Scaffolding also supports students who struggle with self‑regulation.

3. Require transparency logs: 

   • Ask students to record prompts, model outputs and their decisions to keep, modify or discard AI suggestions.

   • Logs make the process visible and support metacognition without turning instructors into detectives.

4. Focus on process and reflection: 

   • Reflective assignments and learning journey logs show how students think and learn. 

   • Reflections also discourage plagiarism by valuing the journey over the final product.

5. Personalize and contextualize prompts: 

   • Highly personalized instructions and local data reduce AI’s usefulness

   • Connecting tasks to students’ experiences and communities encourages original thinking.

6. Provide clear policies: 

   • Communicate when AI is encouraged, permitted with attribution or prohibited for specific tasks, and explain why. 

   • Clear expectations foster ethical use and transparency. For samples and phrasing, see Supervising with AI in 2025.

These principles underpin the eight examples that follow.

1. AI Audit and Critique

Goal: Train students to critically evaluate AI outputs and correct them.

Brief: Students prompt an AI tool for a 250–300 word mini-review on a focused question. They then verify every claim, citation, and inference, and produce a corrected version with an explanation of changes.

• Allowed AI tasks: initial summary, outline of claims.

• Banned AI tasks: writing the corrected analysis or the reflective memo.

• Deliverables:

  • Annotated AI output with tracked corrections and source links

  • 400–600 word reflection on failure types and how verification changed the argument

  • Prompt log with timestamps

• Integrity controls: one 5-minute oral check-in that focuses on a corrected claim and its sources.

• Rubric notes (10 pts): verification rigor (4), clarity of corrections (3), reflection quality (2), documentation completeness (1).

• Helpful reading for students: How Professors Detect AI in Academic Writing and Ethical Use Cases of AI in Academic Writing.

Example Brief #1: To guide the audit, offer students targeted process feedback first, then have them verify claims and supply sources. The screenshot below shows thesify Feedback flagging an unmet comparison requirement in a student’s paper. Use it to model how students should connect feedback to revisions and to their prompt log.

Use this to link feedback to a concrete revision plan and to the prompt log. Students note what they changed and which sources support the comparison.

You can also coach analysis quality. The second screenshot of thesify’s feedback highlights when evidence is reported without interpretation in a student’s paper. Ask students to use thesify on their own draft and revise one passage by adding analysis, then attach a citation that supports the reasoning.

Ask students to transform a quote into analysis and to supply a source that justifies the inference. Add the change to the audit log.

Example Brief #2: To make the audit concrete, start with a short AI-generated passage that looks persuasive, then require students to verify every claim, trace sources, and rewrite what cannot be supported. The goal is not to punish the use of AI, it is to teach how to test arguments, identify missing evidence, and record decisions transparently.

The two examples below show how a general assistant can produce confident analysis in seconds. Use them as critique targets in class. Ask students to label each sentence as supported, unsupported, or misinterpreted, then replace or revise the prose and attach citations.

ChatGPT supplies analysis and causal claims that look finished. Students should annotate each claim, supply sources, or rewrite unsupported lines.

Treat these bullets as a checklist for verification. Require students to trace sources for each point or revise claims that lack evidence.

Point students to your disclosure note and to thesify’s Ethical Use Cases of AI in Academic Writing for how to document AI assistance.

Paraphrase tools can also shift meaning. Build a mini-task where students compare an AI rewrite to the original passage, highlight changes in claims or terminology, and correct the record with page-accurate citations. Below shows an example from the tool JenniAI. 

Use for a “spot the drift” exercise. Students mark wording changes that affect meaning and insert cited corrections.

Why it works: 

• This pattern turns AI into a case study. Harvard recommends fact‑checking AI essays and documenting prompts and edits.

• Carleton’s suggestions for critical reading—asking what the AI left out, what biases appear, and how it paraphrases sources—also align with this pattern. 

• Graduate students develop research literacy, practise referencing and learn to articulate their reasoning.

Implementation tips:

1. Provide students with a rubric that weighs accuracy of corrections more than the AI’s original content.

2. Use peer review sessions where students exchange annotated AI outputs.

3. Require a reflective memo on how their perception of AI changed through the exercise.

2. Literature Mapping with AI

Goal: Teach students to use AI for discovery while maintaining scholarly rigor.

Brief: Students use AI to propose a concept map for a subfield, then rebuild that map using peer-reviewed sources and explain each change.

Before you build clusters, have students open, for example thesify’s PaperDigest on a key article to extract claims, methods, and keywords. This creates a common starting point for the map while keeping the workflow inside peer-reviewed sources.

Use thesify’s Digest to pull candidate keywords and claims into your concept map. Students must still verify relevance and add overlooked seminal work.

Discovery should keep students inside scholarly sources. The screenshot below shows an example from thesify’s Resources panel, which surfaces peer-reviewed items tied to their working draft. Ask students to select and validate sources for each map cluster, then log which suggestions they rejected and why.

Use to build clusters, verify relevance, and record rejected items. This supports a transparent literature map.

• Allowed AI tasks: 

  • Article summaries

  • Draft concept map

  • Cluster labels

  • Alternative keywords.
    
    

• Banned AI tasks: 

  • Writing the narrative synthesis.
    
    

• Deliverables: 

  • Revised map, 800–1000 word narrative with in-text citations

  • Change log linking map edits to sources
    
    

• Integrity controls: 

  • Minimum five verified sources per cluster

  • List of AI-suggested items that were rejected and why.
    
    

• Rubric notes (10 pts): accuracy of clusters (3), coverage and recency of sources (3), quality of change log (2), clarity of narrative (2).
  
  

Why it works: 

• AI often suggests popular or recent articles, but may ignore older seminal works. 

• By verifying and expanding the AI list, students learn to question AI authority and to build a coherent literature synthesis. 

• The visual map encourages systems thinking.

Implementation tips:

1. Teach students to use reference management tools and to annotate AI suggestions clearly.

2. Evaluate the completeness of the map and the quality of the narrative, not the AI output itself.

3. Encourage students to collaborate on maps to reduce workload and produce richer networks.

3. Replicating and Red‑Teaming

Goal: Combine replication studies with adversarial thinking.

Example brief: Students choose a recent peer‑reviewed study or computational model relevant to the course. With AI assistance, they replicate the analysis or experiment, documenting each prompt and code snippet. Once replicated, they “red‑team” by intentionally seeking flaws: testing sensitivity to alternative parameters, exploring edge cases, and searching for ethical concerns.

• Allowed AI tasks: initial plan outline, list of potential failure points.

• Banned AI tasks: writing the final replication plan.

• Deliverables: failure table, corrected plan, 5-minute viva focusing on assumptions and design choices.

• Integrity controls: instructor supplies the target paper, students must identify at least three nontrivial risks introduced by AI shortcuts.

• Rubric notes (10 pts): depth of risk analysis (4), quality of corrections (3), oral defense (2), documentation (1).

Why it works: 

• Replication fosters methodological rigour, while red‑teaming develops critical thinking and ethical judgement. 

• See teaching suggestions in Teaching with AI in Higher Education (2025), which includes model comparison memos and AI‑to‑human handoffs; this pattern builds on those ideas by having students critique both the AI and the original study.

Implementation tips:

1. Provide guidance on accessing datasets and handling reproducibility issues.

2. Organise teams with diverse expertise (methods, coding, subject knowledge) to share workload.

3. Grade both the replication accuracy and the depth of red‑team analysis.

4. Oral Defence and In‑Person Checkpoints

Goal: Assess students’ understanding and communication skills beyond written outputs.

Example brief: Students produce an AI‑assisted research proposal or design (e.g., a grant proposal with AI‑generated literature summary). They then participate in a timed, device‑free oral defence where they explain their choices, respond to questions and demonstrate mastery of key concepts. At least one checkpoint occurs mid‑project where they outline the scope and answer clarifying questions.

• Allowed AI tasks: outline, phrasing suggestions, copyediting.

• Banned AI tasks: argument development, analysis paragraphs, discussion sections.

Outlining and phrasing support can be allowed, but final argument and conclusion writing should remain the student’s work. The image below of ChatGPT is a good teaching example. Have students show how they used AI for phrasing, then defend their analytical choices orally.

Keep conclusions student-written. The oral defence verifies authorship, choices, and sources.

• Deliverables: draft with AI usage log, 6–8 minute defence, rubric sheet completed by peers.

• Integrity controls: compare style to prior work, ask the student to explain a paragraph they wrote and a change they rejected from AI.

• Rubric notes (10 pts): clarity of argument (3), quality of defense (3), adherence to AI boundaries (2), documentation (2).

Why it works: 

• Harvard notes that in‑person exams, oral presentations and timed checkpoints are more AI‑resilient than take‑home essays.

• Implement oral assessments that align with this pattern. Oral defence forces students to internalise and articulate content, making it harder to rely on AI.

Implementation tips:

1. Provide students with question themes (methods, data, ethics) but keep specifics unknown until the defence.

2. Record sessions (with consent) and make transcripts available for accessibility and grading transparency.

3. Encourage reflective self‑assessment post‑defence.
   
   

5. Data Documentation and Model Cards

Goal: Instil ethical practices around data and model use.

Example brief: Students receive a dataset or train a small model (e.g., classification of tweets). They use AI to draft a data sheet or model card, including description of the dataset, collection process, demographic considerations, potential biases, evaluation metrics and limitations. They then revise and expand the draft, adding citations, and ethical analysis.

• Allowed AI tasks: suggestions, small code snippets, test cases.

• Banned AI tasks: writing the final analysis narrative.

• Deliverables: before-after diffs, decision log, 1-page rationale connecting choices to statistical or computational principles.

• Integrity controls: require runnable artifacts or traceable screenshots, include a reproducibility checklist.

• Rubric notes (10 pts): correctness and performance impact (4), justification quality (3), documentation (2), reproducibility (1).

Why it works: 

• The pattern echoes industry standards for transparency (e.g., dataset and model cards).

• It leverages AI for initial drafting but requires human insight. 

• Harvard advocates source‑anchored critiques; here, the critique targets data documentation.

Implementation tips:

1. Provide templates and examples. Evaluate whether students identify biases and limitations beyond what AI suggests.

2. Consider group assignments to share workload.

3. Use the assignment to discuss responsible data use and privacy.

6. Methodology Translation for Multiple Audiences

Goal: Develop communication skills across audiences.

Example brief: Students select a complex method (e.g., a Bayesian hierarchical model) and ask AI to generate explanations for peers, policymakers and the general public. They edit each explanation for accuracy, tone and depth, explicitly correcting any hallucinations and adding analogies.

• Allowed AI tasks: first-pass lay summaries, tone alternatives, headings.

Style assistance can save time, but students should justify their final choices. Ask them to show the AI’s suggestions, then explain what they kept or changed to fit the audience and the field’s terminology. For example, the screenshot below shows JenniAI suggesting a unique title for a student’s paper. 

Allow style ideas, require a justification log. Final titles should reflect actual claims and methods.

• Banned AI tasks: final domain-specific terminology choices.

• Deliverables: three audience versions, 300-word reflection on changes and trade-offs, terminology check table.

• Integrity controls: spot-check terminology against source papers, include a misinterpretation checklist.

• Rubric notes (10 pts): accuracy retained across versions (4), clarity and appropriateness of tone (3), reflection (2), documentation (1).

Why it works: 

• Translation tasks require deep understanding. 

• Harvard encourages AI‑to‑human handoffs, and this pattern extends that idea by emphasising audience awareness. 

• It is particularly useful in interdisciplinary courses and policy programmes.

Implementation tips:

1. Provide examples of effective science communication and discuss cognitive load for each audience.

2. Grade based on clarity, appropriateness, and evidence of correcting AI errors.

3. Encourage students to submit their public‑facing piece to a blog or newsletter to foster authentic dissemination.

7. Code and Analysis Review

Goal: Use AI as a pair‑programming partner while maintaining human oversight.

Example brief: Students are given a codebase (e.g., a simulation or data analysis script). They ask AI to suggest improvements—optimising functions, refactoring for readability or recommending new analyses. For each suggestion, students decide whether to accept, modify or reject, documenting their reasoning and testing the outcome. They compile a table summarising suggestions and results.

• Allowed AI tasks: suggestions, small code snippets, test cases.

• Banned AI tasks: writing the final analysis narrative.

• Deliverables: before-after diffs, decision log, 1-page rationale connecting choices to statistical or computational principles.

• Integrity controls: require runnable artifacts or traceable screenshots, include a reproducibility checklist.

• Rubric notes (10 pts): correctness and performance impact (4), justification quality (3), documentation (2), reproducibility (1).

Why it works: 

• Pair‑programming with AI fosters coding literacy and critical evaluation. 

• Harvard proposes AI‑to‑human handoffs and model comparisons; this pattern operationalises those ideas in a computing context. 

• It also draws on students’ needs for personalised instruction

Implementation tips:

1. Encourage students to run AI suggestions through version control so instructors can see changes.

2. Evaluate not just whether suggestions were adopted but why.

3. Provide accessibility by allowing oral reasoning for students who find academic writing challenging.

8. Reflective Process and AI Journey

Goal: Promote metacognition and ethical awareness through documentation.

Brief: For any substantial project, students maintain a log of every AI interaction. The log includes the prompt, the AI output, a note on whether they used the suggestion, and a reflection on what they learned. At the end, they write a narrative about their learning journey and how AI shaped it.

• Allowed AI tasks: 

  • Using AI to draft tentative questions 

  • Generating formative feedback on the student’s own writing

• Banned AI tasks: 

  • Writing the final reflection 

  • Back-filing the log 

• Deliverables:

  • Prompt log

  • One-page learning narrative

  • Two before-after excerpts linked to feedback

• Integrity controls: require links to publisher pages or DOIs, no screenshots of Google results as proof.

• Rubric notes (10 pts): accuracy of verification (4), clarity of table (3), reflection (2), documentation (1).

Example Brief Mini-Task #1: Process evidence makes learning visible. Use formative feedback suggested by AI to structure what students revise and how they document AI decisions. For example, use thesify’s downloadable feedback report as a starting point for your students. 

Students respond to concrete feedback in their prompt log and reflection, which you can grade for specificity.

Include a short “rewrite with evidence” checkpoint. Students attach a before-and-after excerpt and a one-paragraph note that lists which AI suggestions they rejected and which claims they supported with sources.

Example Brief Mini-Task #2: This task can also be used with tools less likely to meet university AI-use standards. For example, the below screenshot shows JenniAI making substantial text rewrites for a student paper. However, this can be used as a learning opportunity.

Good for a citation-integrity reflection. Require page-accurate references and a sentence explaining how the rewrite changed emphasis.

Why it works: 

• Reflective assignments and process logs make learning visible. 

• They align with Colorado State’s AI‑developed work and encourage metacognitive awareness. 

• The log demystifies AI and reduces temptation to hide its use.

Implementation tips:

1. Provide a log template (table or digital form). Consider awarding points for completeness rather than “right” answers.

2. Encourage students to discuss challenges and insights rather than just successes.

3. Use logs for formative feedback during the project.

Combining Patterns and Adapting by Discipline

Graduate programmes vary widely, so instructors should adapt and combine patterns:

Humanities: 

1. Pair the AI audit with oral defence. 

2. Students might use AI to generate an initial literary analysis, audit it, and then present their revised interpretation orally. 

3. Include reflective logs to capture their analytical journey.

Sciences and engineering: 

1. Combine literature mapping, replication/red‑teaming and code review. 

2. Students map the field, replicate an experiment with AI assistance, test the robustness and document all AI prompts. 

3. They then produce a model card for their final output.

Social sciences: 

1. Use methodology translation and community‑based tasks. For example, in a public policy course, students may use AI to draft policy briefs for different stakeholders and then critique AI’s framing. 

2. Include local data to make assignments unique and challenge AI’s knowledge.

Allow students to receive AI generated feedback on their own writing and research. Require a one-page revision plan that lists two “Not answered” topics and one “Can be improved” topic, based on the AI feedback. Students attach before-after text and reference entries.

Use suggested topics to steer the next drafting session and to structure the oral check-in.

Professional programmes (law, business, medicine): 

1. Include data documentation and oral defence to address ethics and accountability.

2. Encourage students to connect assignments to real cases and personal experiences.

3. Allow students to receive AI feedback on their own writing and research and require proper process evidence of AI use.

Students can link each revision to a thesify feedback item, then document the change in the log. Grade specificity and evidence, not polish alone.

Implementation Tips and Workload Management

1. Start with a pilot. Run one example as a low-stakes task before using it in graded work.
   
   

2. Publish your boundaries. Place a short AI policy and disclosure expectation in the syllabus, then link to a longer version in your LMS. For models, see the baselines described in Supervising with AI in 2025.

Ask students to keep their drafting and discovery in one place and export a prompt log with each submission. The screenshot shows how thesify keeps the draft, suggested sources, and feedback together so you can audit decisions quickly.

A transparent workflow that you can review. Have students submit the exported log and the selected source list with the final draft.

1. Grade the process, not only the product. Give points for logs, verification, and oral explanations. This approach is outlined in the article How AI Is Changing Academic Grading for Professors.

2. Use peer review in class. Have students exchange logs and maps, and require one question peers want the author to defend orally.

3. Build a short integrity checklist. One page, used across all examples, that asks: what did AI do, what did you do, how did you verify, what did you reject.

4. Consider accessibility early. Provide templates in accessible formats and allow alternatives for oral components when needed.

FAQs on Alternatives to AI Banning Assignments

1. How can I allow AI in assignments without encouraging shortcuts? 

Give AI a narrow, named role, then require students to verify, document, and defend choices. The combination of logs, evidence, and an oral checkpoint removes easy shortcuts and supports learning, see Teaching with AI in Higher Education (2025).

2. Do I still need detection tools?

Detection may be one signal, but it is imperfect. A process-based design that asks for drafts, logs, and brief explanations reduces reliance on detectors and promotes fairness, see How Professors Detect AI in Academic Writing.

How do I grade AI-assisted work fairly?

Score verification rigor, the quality of corrections and justifications, and the clarity of reflection. Emphasize process evidence over polished prose, see How AI Is Changing Academic Grading for Professors.

What should my disclosure text say?

Keep it short and specific, for example which steps used AI, which prompts were used, and what you verified or rewrote. For examples and framing, see thesify’s Substack and the disclosure section in supervising with AI.

AI-Inclusive Graduate Assessment Templates You Can Copy

Use this generic template across the examples above. Edit the bullets to match the specific task.

AI Scope Statement (to paste in your brief):

• You may use AI for: brainstorming, outlining, surface-level phrasing suggestions.

• You may not use AI for: analysis paragraphs, results and discussion sections, final reflective writing.

• You must include a prompt log with timestamps and a list of AI suggestions you rejected.

Deliverables:

• Artifact, for example map, corrected text, plan, code diff, documentation card.

• Process evidence, for example prompt log, verification table, change log.

• Reflection, 300–600 words on what AI got wrong, what you changed, and why.

Integrity Controls:

• One short oral explanation focused on a claim, a change, or a design choice.

• Source verification rules, for example DOIs or publisher links.

• Data and privacy reminder, do not upload confidential or regulated content.

Rubric Sketch (10 pts total):

• Verification or testing rigor, 3–4 pts

• Quality of corrections or decisions, 2–3 pts

• Reflection clarity and specificity, 2 pts

• Documentation completeness, 1–2 pts

Toward Responsible AI‑Inclusive Assessment

Banning AI outright is a losing game. Instead, graduate instructors can harness AI’s potential while maintaining academic integrity by designing assignments that foster critical thinking, research rigour and reflective practice. The sample assignments in this guide provide concrete ways to integrate AI into coursework, ensuring students learn with AI rather than outsourcing their learning to it. 

By combining audits, mapping, replication, oral defence, documentation, translation, coding review and reflective logs, instructors can create rich learning experiences tailored to their discipline. Try adopting one assignment in your next course and share your experiences with colleagues. For more on teaching with AI and ethical use, explore our posts on thesify’s blog and Substack.

Explore Ethical AI Academic Tools

Want to try these assignments in your course? Pair them with, thesify, an academic AI tool designed to support academic integrity. Sign up now for free to check out thesify’s suite of academic tools that support transparent AI workflows and help you design rigorous assessments.

Related Resources

• Responsible AI Academic Writing Tools – From Panic to Policy: Instead of succumbing to an “AI panic,” educators are shifting toward proactive policies that embrace responsible AI academic tools while safeguarding academic standards. This blog post serves as a comprehensive roadmap for professors, instructors, and academic administrators to craft a clear syllabus policy on AI – one that maintains ethical AI for students, preserves academic integrity, and leverages AI’s potential as a learning aid rather than a cheating shortcut.

• AI Academic Writing Ethics: How Professors Can Teach Responsible AI Use: What is ethical AI use in academic writing? Ethical AI use in academia means leveraging AI tools in a way that supports learning, originality, and honesty. In simple terms, AI should assist the writing process – not do the work for the student. For example, using AI to brainstorm ideas, check grammar, or get feedback is generally considered responsible, whereas submitting unedited AI-generated text as one’s own is academic misconduct. In this post, discover case studies of ethical AI integration in higher education, university initiatives promoting responsible AI use, real-world examples of ethical AI application in the classroom, and further resources on AI ethics in academia.  

• AI in Higher Education 2025 – A Professor’s Guide to Adapting Teaching and Policy: The 2025 HEPI Student AI Survey findings matter to educators: the classroom landscape is shifting, and professors must respond with updated strategies in teaching, assessment, and policy. Discover how professors can effectively integrate generative AI tools into teaching, assessment, and skill development to better support students in 2025. We cover the particularly progressive approach of openly permitting students to utilize AI tools in specific assignments, but explicitly grading them on how they engage with, document, and critically reflect upon their AI usage.