Agree on AI rules before the study session starts: check the relevant course and assignment policies, define which study tasks are allowed, and decide how to verify and disclose any AI contribution. A group agreement helps members coordinate; it cannot authorize use that an instructor, assignment, or institutional policy prohibits.
Start with the rules that govern the course
Before choosing a tool or prompt, check the syllabus, assignment instructions, and current instructor guidance for the course. Policies can differ by institution, course, assignment, and task. If you cannot tell whether a proposed use is allowed on assessed work, ask the instructor before using AI.
Official policies illustrate why groups should not assume one campus’s rule applies everywhere. Princeton tells students to determine whether, how, and to what extent a course allows generative AI, and notes that requirements can vary by course and field: Princeton’s guidance on disclosing generative AI use. Dartmouth’s policy for affected undergraduates says submitted coursework may not use GenAI unless it is expressly permitted: Dartmouth’s coursework guidelines. These are institution-specific examples, not a universal college policy.
Agree on the purpose and permitted tasks
Write down what the group is trying to learn, then name the AI activities that fit the applicable course rules. A clear session goal might be to compare interpretations of a concept, test understanding, or make practice questions. The University of Chicago Biological Sciences Division lists comprehension testing and practice problems as acceptable independent-study uses under its guidance; that example does not establish permission in other programs or for graded work.
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Be specific about boundaries. For example, a group could allow AI to generate practice questions while forbidding it from solving a current assignment—but only if the relevant instructor or policy permits that use. Some course rules require advance approval; others define permitted uses in advance. Do not treat a group vote as approval.
Set disclosure and ownership expectations
If AI contributes to assessed work, each student should follow the instructor’s required method for acknowledgment, disclosure, or citation. Do not assume that one citation format works across courses. Princeton provides a disclosure template that asks students to identify the tool and its purpose, while Dartmouth gives examples of acknowledging AI use; both direct students to follow relevant course or field expectations.
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Make clear that each student is responsible for their own submission. Group members should not present AI-generated material as their own work, and should be able to explain any material they choose to use. Columbia Law School’s policy for academic year 2026–27, effective August 1, 2026, says students should be transparent and err on the side of disclosure when work relies on an AI tool. That policy applies to Columbia Law School, not to colleges generally.
Build verification into the session
Treat generated answers as claims to check, not as authoritative course material. Compare them with assigned readings, lecture notes, or other authoritative sources, and open any cited source to confirm that it exists and supports the claim. The University of Chicago Biological Sciences Division says students remain responsible for errors, invented references, and flawed conclusions, including when they originate with a tool. Columbia Law School’s 2026–27 policy likewise requires independent verification of AI-generated sources and outputs.
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- Check factual claims against course materials or a reliable source.
- Open and read citations instead of trusting a generated bibliography.
- Ask each member to explain the concept in their own words before relying on an AI-assisted answer.
Protect classmates’ and course information
Agree not to enter sensitive or restricted material into an AI service unless the institution has approved that tool for that data. Examples in the University of Chicago Biological Sciences Division guidance include personally identifiable information, FERPA-protected student data, exam content, and restricted research information. Local rules determine what tools may be used for particular data, so do not assume that an institutional account makes every use acceptable.
Use a short agreement for each study session
At the start, take a minute to settle these points. The agreement should be specific to the course and task, and members should revisit it when the assignment or policy changes.
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- Course rule: Identify the syllabus, assignment instruction, or instructor guidance that applies. If permission for assessed work is unclear, pause and ask.
- Learning goal: State what the group is studying, such as reviewing notes or practicing a concept.
- Allowed AI activity: Name what the group may use AI for and what it may not do, within the course’s rules.
- Disclosure: Agree that any AI contribution to submitted work will be disclosed in the required format.
- Verification: Decide how members will check answers, claims, and references against reliable sources.
- Privacy: Keep personal, protected, exam, unpublished, and otherwise restricted information out of unapproved tools.
- Ownership: Before using material in individual work, each student confirms they can explain it, verify it, and identify AI’s contribution.
When group members are in different courses
Do not negotiate a single rule and apply it to everyone. Each person should check the policy governing their own course and assignment. Compare the relevant requirements before collaborating on any assessed work:
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- Authority and scope: Which institutional, program, course, assignment, or exam rule applies?
- Task: Does the rule distinguish personal study and practice from drafting, answers, or other assessed work?
- Disclosure: Is use acknowledged or cited, and in what format?
- Data: Is the material public, personal, protected, or restricted, and is the chosen tool approved for it?
- Responsibility: Can each student explain and verify the work they will submit?
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