Guided AI use is bounded, purpose-led permission. An instructor decides where an AI system can support a particular learning activity, where it would bypass the skill being assessed, what students must disclose and verify, and what evidence will show that they learned. The rules belong in the assignment instructions, not only in a general syllabus statement. They can permit brainstorming in one task, require a named tool for a defined component in another, and prohibit generated prose where independent writing is the outcome.
Guided AI use is an assignment-level design decision
There is no single cross-institutional definition of “guided AI use.” Monash University uses guided use as a policy option in which an instructor permits AI for purposes described in the assessment instructions and requires students to document and acknowledge that assistance. Northern Illinois University (NIU) describes a similar “Limited or Guided AI Use” approach, such as allowing brainstorming, outlining or clarity editing while setting further conditions. These are examples of institutional practice, not a universal academic standard. (Monash University; Northern Illinois University)
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In practical terms, guided use answers five questions before a student starts:
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- What learning outcome is this task meant to develop or measure?
- Which AI activities are allowed, required, optional, restricted or prohibited?
- Does the rule apply to the whole assignment or only to a component?
- What must the student disclose, preserve and check?
- What process evidence will demonstrate the student’s own understanding?
“AI” also needs a usable scope. A policy may cover predictive text, grammar suggestions, generative text, image or audio generation, code assistants, translation, search summaries and chatbots differently. The University of Connecticut advises instructors to define the term and explain the relevant categories rather than assuming students interpret “AI” in the same way. (University of Connecticut)
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Start with the learning outcome, not the tool
Identify the knowledge or skill being assessed
Write down what students should know or be able to do after the task, what the assignment contributes to later learning and which parts require independent practice. Utah Valley University (UVU) recommends considering the assignment’s skill and knowledge goals, its context and individual learning goals before deciding whether AI is appropriate. (Utah Valley University)
If generated text would replace the very practice you need to observe—such as constructing an argument, interpreting primary evidence or writing code from concepts taught in class—limit or prohibit that use. If the outcome is evaluating sources, revising a draft or comparing explanations, a carefully bounded AI activity may provide useful material to critique. NIU’s guidance puts pedagogical purpose ahead of a blanket permission or ban. (NIU)
Separate learning practice from final-product assistance
A student might use AI to generate possible research questions, receive comments on a student-written paragraph or produce a first draft. Those activities do not provide the same evidence of learning. State the distinction explicitly: for example, “You may ask for ideas and clarity feedback, but you may not submit AI-generated paragraphs as your analysis.”
Choose a permission model that fits the task
The following models are policy choices, not competing products. Select one per assignment or component and explain the educational reason.
| Model | Typical permission | Best fit | Required safeguards |
|---|---|---|---|
| Restricted or prohibited | No generative AI, or no AI for a named component | Demonstrating unaided competence, timed practice or personal performance | Define what counts as AI, provide an allowed alternative and explain why independent work is necessary |
| Limited or guided | Specific uses such as brainstorming, outlining, examples or feedback; other uses are not allowed | Tasks where a selected tool can support learning without replacing the assessed skill | Assignment-level boundaries, disclosure, verification and process evidence where needed |
| Encouraged with transparency | Students may use a named tool or category across a defined workflow | AI literacy, critique, iterative design or professional workflows that are themselves outcomes | Prompt and revision records, source checking, privacy guidance and an explanation of the student’s decisions |
Monash recommends a “recipe”-like specification: identify the tools and processes, then label each element required, recommended or optional. A course can use different models for different assignments. (Monash University)
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A seven-step workflow for writing the policy
1. Name the outcome and the evidence
Write a sentence such as, “This report assesses your ability to compare two explanations using evidence from the assigned readings.” Then decide which artifact, explanation or performance will prove that ability. If an AI-generated comparison could satisfy the task without the student doing the comparison, redesign the activity or restrict that use.
2. Map permissions to each task component
List the components separately: question formation, source search, calculations, coding, drafting, editing, translation and presentation. Mark each as allowed, required, optional or prohibited. For example: “Brainstorming and an outline are allowed; generated prose is not. You may use an AI tool to suggest test cases for your own code, but you must write and explain the final implementation.” UVU’s framework emphasizes matching the level of assistance to the assignment’s purpose. (UVU)
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Give a short rationale tied to the outcome: “The analysis section must be your own reasoning so that I can assess evidence-based interpretation. AI feedback is allowed on a draft because revision is an outcome.” Monash and NIU both frame restrictions around the human knowledge or skills the assessment must demonstrate. (Monash; NIU)
4. Specify tools, access and cost
Name a permitted tool or describe the category if several tools are acceptable. Tell students whether an account, paid tier, particular device or internet connection is required, and offer an equivalent non-AI route when it is not. UConn recommends communicating required purchases or access; UVU cautions against assuming equal experience or access. (UConn; UVU)
5. State disclosure and record-keeping rules
Say whether acknowledgment is required, where it belongs and what it must contain. A workable record can include the tool name, date, purpose, important prompts, selected outputs and a brief account of what the student changed or rejected. Monash describes documenting prompts, iterations and how output was revised or integrated. Match the format to disciplinary conventions instead of assuming one citation style covers every use. (Monash; UVU)
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6. Require verification and retain responsibility
Tell students to check factual claims, quotations, calculations, citations and links against reliable sources; look for bias, missing context and invented references; and revise or discard unsupported output. The University of South Florida (USF) states: “Students are responsible for the accuracy of their submissions and must cross-verify the information produced by these tools with reliable sources.” That page was last updated in August 2025. (USF) NIU’s sample language similarly says, “Students remain accountable for meeting course learning outcomes, regardless of AI assistance.” (NIU)
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When a final artifact cannot establish whether the intended learning occurred, add proportionate evidence: a proposal, annotated sources, drafts, revision notes, a short reflection, an oral explanation, an in-class application or a critique of AI output. NIU and the University of Oregon present these as options to align with outcomes, workload, discipline and student context—not as a universal anti-cheating recipe. (NIU; University of Oregon)
What a student-facing instruction can look like
Place the rule next to the assignment, using plain language. For example:
Learning outcome: evaluate competing explanations with evidence from the assigned readings.Allowed: use an AI tool to brainstorm questions and to comment on a draft you wrote. Do not submit generated analysis or citations. Verify every factual claim and citation in the library databases or assigned sources.Disclosure: add a note after the references naming the tool, describing its purpose, listing the prompts or a link to the conversation when available, and explaining what you kept, changed or rejected. Ask the instructor before using a case not covered here.The example distinguishes assistance from authorship, identifies verification and gives a path for uncertainty. Adapt wording to institutional academic-integrity, accessibility, privacy and disciplinary requirements; university examples do not override local rules. (USF; UConn)
Assess learning when AI is part of the workflow
Use checkpoints that reveal decisions
Require a question proposal, source map or outline before drafting; collect a revision history or short process memo; and ask students to explain one important choice. For a coding task, assess tests, debugging notes and an explanation of the algorithm. For a design task, assess alternatives and the reasons for rejecting them. These checkpoints should be weighted only when they represent the stated outcome.
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Make critique an assessable skill
Provide a deliberately flawed AI response and ask students to identify unsupported claims, missing evidence, stereotyping or faulty reasoning. This can assess verification and disciplinary judgment without treating fluent wording as proof of accuracy. Require students to cite the sources used to correct the response.
Use oral or applied demonstrations selectively
A brief explanation, practical demonstration or supervised application can confirm that a student understands submitted work. Choose it where the outcome warrants the time and provide accessible alternatives. Process evidence should increase validity, not create an unnecessary burden for every assignment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Protect privacy and account for unequal access
Do not tell students to upload confidential records, identifiable student work, grades, unpublished research or non-public teaching materials to a public AI service unless the institution has approved that use. NIU specifically cautions against uploading such material without institutional approval. (NIU) Follow your institution’s data-classification, records-retention and accessibility rules.
Explain what information a tool receives, whether an account is needed and what alternative is available. A no-cost option may still require a phone number, collect prompts or be inaccessible to a student using assistive technology. UConn and UVU both identify cost, access and differing prior experience as design considerations. (UConn; UVU) Never penalize a student for declining a non-required service when an equivalent approved path exists.
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Handle ambiguity before it becomes a conduct dispute
- “Editing” is unclear: specify whether spelling and grammar suggestions are allowed, whether rewriting sentences is allowed and whether translation counts as assistance.
- A tool adds AI automatically: tell students whether predictive text, smart compose, image enhancement or code completion falls within the rule.
- The tool changes during the term: state a category and the required capabilities, then require students to ask before using a materially different service.
- The assignment has mixed requirements: label permissions beside each component instead of relying on one course-wide sentence.
- A student is unsure: provide a contact route and say that asking before submission is preferable to guessing.
Keep an internal record of policy changes and communicate the effective date. A clear version history helps students and instructors apply the same boundary to work completed under the same instructions.
What current institutional evidence can—and cannot—show
The University of Oregon’s Teaching Support and Innovation page synthesizes emerging studies and concludes that guided use with clear goals and checkpoints can produce better outcomes than open-ended use. It does not establish that guided AI always improves learning; results depend on the task, scaffolding, learner context and assessment. (University of Oregon)
The same page reports local findings from its TEP GenAI CAIT project: only one in 10 UO students interviewed felt comfortable asking an instructor to clarify an AI policy. It also describes roughly a quarter of surveyed UO students as “skeptics,” roughly another quarter as “enthusiasts” and just over half as “pragmatists.” These are project-specific interview and survey descriptions, not representative national statistics, and the page’s publication year is not confirmed. (University of Oregon) The practical implication is to invite questions and make the permission visible where students make decisions.
Student-facing checklist
Before publishing an assignment, verify that students can answer all of these questions:
- Which AI uses are allowed, restricted, required or prohibited?
- Do permissions change across assignments or components?
- Which tools or categories are permitted, and are there cost or access requirements?
- What must be disclosed or cited, and where should the disclosure appear?
- How must students verify information and remain responsible for it?
- What evidence of process or understanding will be assessed?
- What data must not be entered into an AI service?
- What should a student do when a use case is not covered?
- How does the policy connect to the skills and knowledge being taught?
This checklist brings together recommendations from Monash, NIU, UConn and USF. Apply it alongside your institution’s academic-integrity, privacy, accessibility and platform rules. (Monash; NIU; UConn; USF)
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