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Start with a shared baseline and assignment-level rules
An institutional policy should establish common expectations for academic integrity, privacy, transparency, equitable access, and faculty responsibility. It should also make room for instructors to set rules that fit their learning outcomes. Cornell’s committee report describes three options for faculty: prohibit AI use, permit it with attribution, or encourage it. Cornell’s framework treats these as adaptable choices, not one rule that must apply to every assignment. Cornell’s committee report recommends that student use on assignments be allowed only when the instructor expressly permits it.
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A course syllabus can state the overall approach, but students need to see the applicable rule where they make a decision: in the assignment instructions and in class communication, too. Cornell’s teaching guidance emphasizes communicating expectations in all three places. Cornell’s guidance on AI and academic integrity and its course-policy icon resource offer examples of ways to make those expectations visible.
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Decide whether to prohibit, allow with attribution, or encourage AI for each assignment by asking what students are meant to learn or demonstrate. A task intended to build a foundational skill may require independent work; another may use AI for exploration, practice, feedback, or a clearly defined part of a larger project. The policy should state the learning rationale, so students can understand why rules differ across tasks.
- Prohibited: Identify the work students must complete without AI and explain which learning objective depends on their own performance.
- Permitted with attribution: Name the allowed task or use, such as brainstorming or editing, and specify how students should disclose assistance.
- Encouraged: Explain the intended use and how students should evaluate or improve the tool’s contribution rather than accept it uncritically.
These categories are policy choices, not a ranking from strictest to best. A college should give instructors examples and support for choosing among them rather than treating one category as suitable for every discipline or assignment. Cornell’s report on generative AI for education discusses aligning AI use with course outcomes.
Tell students exactly what they may do and what they must document
For each course or assignment, write instructions that students can follow without guessing. Address the following points as relevant:
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- Whether AI use is prohibited, permitted for specified tasks, or encouraged.
- Which tools are allowed, and whether students need approval to use another tool.
- Whether brainstorming, translation, editing, coding, research, or drafting is allowed. Avoid a blanket permission that leaves these distinct activities unclear.
- Which parts of the work, reasoning, or process students must produce themselves.
- What attribution or disclosure is required, including the format and where it should appear.
- What students must verify, including factual claims, citations, and references.
- Whether students must retain or submit prompts, outputs, or other evidence of their process.
- How to ask for clarification before using a tool when the instruction is ambiguous.
When use is permitted, students remain responsible for the submitted work. Attribution does not replace checking an output for errors or confirming that cited sources and references are real and relevant. Cornell provides AI attribution guidance for making expectations about disclosure and student contribution clearer.
Use policy icons as quick signals, not substitutes for instructions
Cornell’s course-policy icons cover several practical signals: “AI-FREE,” assignment-specific rules, approved tools only, use with attribution, privacy-protecting limits, and “Explore and Review.” Instructors can combine the icons in syllabi or assignment prompts. Each signal still needs an accompanying explanation of the rule and its rationale; an icon alone cannot define what counts as permitted use. Cornell identifies the icons as licensed under CC BY-NC-SA 4.0. See the Cornell AI course-policy icons.
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Review tools for privacy, accessibility, and equitable access
A policy should say which tools students may use and what information they may enter. Cornell advises using campus-approved tools when sensitive or proprietary information is involved, and its policy icons include a limit against uploading copyrighted or proprietary class materials unless otherwise specified. Colleges should explain what data a tool collects and how it may be used, and consider whether required tools are accessible and available to all students. EDUCAUSE’s ethical guidelines for AI also identify privacy, transparency, faculty autonomy, unequal access, and non-AI pathways as policy concerns.
Where a tool has not been institutionally assessed, provide a review route rather than leaving each instructor to decide its security and suitability alone. Cornell’s faculty FAQ describes review of unsupported technologies through local IT, including security, privacy, accessibility, and data protection. Colleges should adapt that process to their own procurement and governance requirements; a vendor’s default settings are not, by themselves, an institutional approval. See Cornell’s faculty FAQ.
Handle suspected misconduct through established procedures
Ordinary academic-integrity rules still apply, but an automated AI-detector result should not be treated as conclusive proof. Cornell’s committee report says it discourages automatic detection algorithms for academic-integrity violations because they are unreliable and cannot provide definitive evidence. Colleges should use their established academic-integrity procedures and contextual evidence, with expectations communicated before students submit work. Cornell’s report also does not recommend AI for student assessment. Read the committee report.
Give instructors implementation support
A policy is more usable when faculty have model language, consultation, and training—not just a rule to enforce. Instructors may need help matching permitted AI use to learning objectives, explaining disclosure expectations, and reconsidering assessment where a task no longer measures the intended skill. A 2025 exploratory analysis of 92 university instructors’ generative AI syllabus policies found that most included rationales and student responsibilities, while fewer invited student feedback. The study describes policy content; it does not show that a particular approach produces better outcomes. See the ERIC record.
Implement and maintain the policy
- Gather the people affected. Ask faculty, students, teaching-support staff, IT, accessibility staff, privacy and security teams, and academic-integrity leaders which decisions the policy must settle.
- Publish the institutional baseline. State expectations for learning goals, privacy, transparency, access, attribution, and student responsibility, while clarifying how instructors may set assignment-specific rules.
- Provide adaptable examples. Offer sample syllabus and assignment language for prohibited, attributed, and encouraged use, including examples of disclosure and verification.
- Set a tool-review route. Explain how instructors can request assessment of tools that have not been approved, and who is responsible for reviewing them.
- Train and consult with instructors. Help faculty align rules with learning outcomes and redesign assessments where appropriate.
- Revisit the guidance. Review it on a schedule and when institutional rules, tool terms, or teaching practices change.
Cornell’s committee report recommends administrative support for instructors and explicit AI language in academic-integrity codes; its faculty FAQ describes a route for reviewing unsupported technologies. Colleges should also check their own academic-integrity codes, privacy and accessibility obligations, collective agreements, and approved-tool rules before adopting policy language.
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