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Guided AI use in course design is bounded, purpose-led permission. An instructor decides where an AI system supports the learning outcome, where it would bypass the skill being assessed, what students may and may not do, and how they must disclose, verify, and remain accountable for the result. The rule belongs in each assignment, not only in a broad syllabus statement.
This approach is neither a blanket “AI allowed” policy nor an automatic ban. It can permit brainstorming in one task, require a specified tool for a data-analysis step in another, and prohibit generated prose when the goal is unaided writing. Local academic-integrity, privacy, accessibility, and platform rules still control. Universities such as Monash, Northern Illinois University, and the University of Connecticut describe versions of this assignment-specific model.
What “guided AI use” means
There is no single cross-institutional definition. Monash presents guided use as a named assessment-policy option: AI is allowed for purposes the instructor specifies, while students document and acknowledge assistance. Northern Illinois University uses the related label “Limited or Guided AI Use,” with examples such as brainstorming, outlining, or editing for clarity. These are institutional approaches, not a universal academic standard.
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In practical terms, a guided policy answers six questions before students begin:
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- Which AI activities are allowed, restricted, required, or prohibited?
- Do permissions change between assignments or between parts of one assignment?
- Which tools or categories of tools may be used, and are accounts or paid access required?
- What must students disclose, where must they disclose it, and what evidence should they retain?
- How must students verify output and remain responsible for claims, code, calculations, images, or citations?
- What evidence will show that the intended learning occurred?
“AI use” should be defined for the course. It may include predictive text, grammar correction, generative text, image creation, coding assistance, translation, or question-answering. A rule that says only “AI” leaves students guessing whether a spelling tool and a chatbot are treated alike. The University of Connecticut specifically notes that the term can cover these different forms of assistance.
Start with the learning outcome
First identify what the assignment is meant to develop or measure. Ask what students should know or be able to do afterward, how the task prepares them for later work, and which parts require independent practice. Utah Valley University recommends examining the assignment’s skill and knowledge goals, context, and individual learning goals before deciding whether AI belongs in the workflow (UVU guidance).
If generating the answer would let a student bypass the very practice being assessed, restrict that use or redesign the task. For example, an assignment intended to assess a student’s ability to construct an argument may allow an AI-generated list of possible topics, but not AI-written paragraphs. A programming exercise intended to assess debugging may permit a tool to explain an error after the student submits a first attempt, while requiring the student to explain the final fix.
Choose a permission level for each task
Use a small set of understandable categories, then add the exact boundaries. The labels below are policy choices, not competing products.
| Approach | Typical permission | Best fit | Evidence and student duties |
|---|---|---|---|
| Prohibited | No generative or specified AI assistance for the task | Work that must demonstrate unaided competence or personal performance | Student produces the work independently and follows local integrity rules |
| Limited or guided | Named uses such as brainstorming, outlining, translation, or feedback on a student-written draft; other uses are not allowed | Tasks where selected assistance supports learning without replacing the assessed skill | Disclosure, verification, and possibly prompts, drafts, or revision notes |
| Encouraged with transparency | AI is a planned part of a component or workflow, with a specified tool or tool category | Assignments in which AI literacy, critique, or tool-supported practice is itself an outcome | Students document use, evaluate limitations, and explain decisions |
| Component-specific | Different rules for research, drafting, analysis, or presentation within one assignment | Complex projects containing both AI-appropriate and AI-sensitive skills | Separate instructions and grading evidence for each component |
Monash recommends a recipe-like specification in which required, recommended, and optional processes are distinct. Northern Illinois University likewise places the pedagogical purpose ahead of a one-size-fits-all rule. State the category in the assignment itself so a student does not have to infer it from a syllabus paragraph.
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Write the assignment rule in operational language
Name permitted activities and boundaries
Replace “AI may be used responsibly” with observable instructions. For example: “You may use an AI tool to brainstorm three research questions and to suggest headings. Do not submit generated prose. Include the prompts and a 150-word note describing which suggestions you kept, changed, or rejected.” A different task might say: “You may request feedback on a draft you wrote. Check every factual statement against the course readings and attach the tool’s response.”
Distinguish among brainstorming, outlining, generating examples, editing for clarity, translating, drafting, paraphrasing, coding, image creation, and citation generation. Say whether each is required, recommended, optional, restricted to a component, or prohibited. Name a permitted tool or tool category when a specific capability matters, and provide a non-AI alternative if access is not universal.
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Connect the rule to the outcome and to the evidence the assignment must produce. Monash frames restrictions around whether an assessment must demonstrate particular human skills or knowledge; Northern Illinois University similarly says the pedagogical purpose should drive the decision. A one-sentence rationale makes the boundary part of the learning design rather than a disconnected compliance demand.
Tell students what to disclose
Specify whether acknowledgment is required, where it belongs, and what details to include. A proportionate record can name the tool, date, purpose, affected section, and the material changes the student made. For substantial use, require prompts, important iterations, and a short explanation of how output was checked and integrated. Monash describes documenting prompts, iterations, and revisions; UVU also recommends acknowledgment of assistance. Follow disciplinary conventions and institutional rules rather than assuming one citation format fits every use.
Make verification and accountability explicit
Fluent output can be inaccurate, biased, incomplete, or based on nonexistent sources. Students remain responsible for submitted claims, calculations, code, references, and images. The University of South Florida’s guidance, last updated August 2025, states: “Students are responsible for the accuracy of their submissions and must cross-verify the information produced by these tools with reliable sources.” Northern Illinois University’s sample policy puts the same responsibility this way: “Students remain accountable for meeting course learning outcomes, regardless of AI assistance.”
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Give students a checking procedure appropriate to the discipline:
- Separate factual claims, interpretations, calculations, quotations, and references in the output.
- Verify each material claim against assigned readings, authoritative databases, primary documents, or other reliable sources.
- Open every cited source and confirm that it exists and supports the statement attributed to it.
- Check for missing perspectives, stereotypes, unsafe advice, privacy violations, and disciplinary errors.
- Revise or remove unsupported material, then record significant corrections in the required disclosure.
Verification is part of the student’s work, not a guarantee that the tool is accurate. Do not promise that guided use always improves learning; outcomes depend on the task, scaffolding, learner context, and assessment design.
Assess the process as well as the artifact
A polished final product may not show whether the intended learning occurred. When that is a concern, collect evidence that matches the outcome and remains feasible for the class:
- Checkpoints: a proposal, annotated source set, outline, or first attempt before tool assistance.
- Draft history: successive drafts with a brief explanation of substantive changes.
- Process note: the prompt, output excerpt, verification steps, and reasons for accepting or rejecting suggestions.
- Reflection: what the student learned, where the tool failed, and how the final work differs from the initial output.
- Oral or applied component: a short explanation, demonstration, or scenario-based task in which the student must use the underlying skill.
- Critique activity: students identify errors, bias, unsupported claims, or design weaknesses in an AI response.
Northern Illinois University and the University of Oregon Teaching Support and Innovation describe these as options rather than a universal recipe. Select them because they align with the outcome, discipline, workload, and student context. A reflection should not be added if it measures writing endurance instead of the course skill.
Account for access, privacy, and student differences
Access and cost
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Privacy and data handling
Give a clear prohibition on entering confidential or restricted information. Northern Illinois University cautions against uploading student work, grades, or non-public teaching materials to public AI tools unless the tool is institutionally approved for that purpose. Apply your institution’s data-classification, accessibility, records-retention, and consent rules. When a task involves real clients, patients, minors, unpublished research, or identifiable student information, provide de-identified or fictional data and explain why.
Different experience levels
Some students will have extensive experience with prompting; others will be using an AI system for the first time or may not be able to use it because of language, disability, privacy, or technology constraints. Explain the learning goal independently of the tool, teach any required workflow, and provide a non-AI route that can meet the same outcome. Do not treat unfamiliarity with a particular interface as evidence that a student lacks subject knowledge.
A student-facing policy checklist
Place a compact checklist in the assignment page or module. Students should be able to answer all of these before starting:
- What may I do with AI, what is restricted, and what is prohibited?
- Do the rules change for another assignment or component?
- Which tools or categories are permitted, and what cost or account requirements apply?
- What must I disclose or cite, and where should the record appear?
- How must I verify factual claims, sources, calculations, code, or images?
- What process evidence will be assessed?
- What information must never be entered into an AI service?
- Whom should I ask if an unlisted use case is unclear?
- How does this policy connect to the knowledge or skill being taught?
Include a default escalation instruction such as: “If you are unsure whether a use is permitted, pause and ask before submitting; do not assume that a tool’s writing, grammar, translation, or search features fall outside this policy.”
How to compare design choices
When reviewing a proposed policy, compare it on six axes:
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| Question | What to examine |
|---|---|
| Outcome fit | Would the permitted use support the stated knowledge or skill, or bypass it? |
| Scope | Are activities and assignment components named precisely enough to act on? |
| Process evidence | Can the instructor see the reasoning, practice, or decisions that matter? |
| Disclosure burden | Is the requested record proportionate to the extent of assistance? |
| Access and privacy | Can every student participate without prohibited data sharing or an unfair purchase requirement? |
| Local rules | Does the policy comply with institutional integrity, accessibility, records, and data requirements? |
A limited permission can fit a task where selected assistance supports learning. Encouragement with transparency can fit a task in which evaluating AI is an explicit outcome. Restriction can fit work intended to demonstrate unaided competence. The instructor should document the educational reason and apply the institution’s governing rules.
Communicate, review, and clarify the policy
Publish the rule before students begin, use the same terms in the syllabus, assignment page, rubric, and class discussion, and show one allowed and one disallowed example. If the tool changes its capabilities or the institution updates its policy, revise the assignment language and tell students what changed. Keep a record of clarifications so that all students receive the same answer.
Clarification must be easy to request. A local finding from the University of Oregon’s TEP GenAI CAIT project reported that only one in ten UO students interviewed felt comfortable asking an instructor to clarify an AI policy. That is an interview result from one university, not a national statistic, but it is a useful warning: a policy that students are afraid to question is not operationally clear. The same page describes roughly a quarter of surveyed UO students as “skeptics,” roughly a quarter as “enthusiasts,” and just over half as “pragmatists”; those categories and proportions are local to that project, labeled TEP GenAI CAIT 25-26, and should not be generalized to other institutions.
What guided use can and cannot promise
Guided AI use can make expectations visible, connect technology to outcomes, and create structured opportunities to practice verification and critique. It cannot guarantee accurate output, equal access, academic integrity, or improved grades by itself. The University of Oregon page synthesizes emerging studies and concludes that guided use with clear goals and checkpoints can produce better outcomes than open-ended use; that is a synthesis-level conclusion, not an independently verified universal effect size.
The practical test is simple: if a student follows the written permission, discloses the assistance, verifies the result, and completes the required process evidence, the instructor should be able to judge the intended learning. If the policy cannot make that judgment possible, redesign the task or narrow the permission.
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