Students are already deciding when and how to use AI. You can help them make those decisions responsibly by giving them clear boundaries, realistic practice, and feedback on their choices. Consider fitting the six strategies below into your existing assignments and activities.
Practical strategies
Start with the learning goal. Identify the reasoning, practice, or judgment students must demonstrate themselves.
Then put three statements directly in the assignment directions:
- You may use AI to…
- You may not use AI to…
- Here are the reasons why…
Place this guidance inside the assignment so students see it when they need it.
Give students an AI response and ask them to decide what they can trust. Use a response tied to a current course topic.
Have students use this five-step check:
- Review: Read an AI response to a course-related question.
- Identify: Mark its factual claims, sources, assumptions, and perspectives.
- Notice gaps: Identify missing context, perspectives, or alternative explanations.
- Verify: Check important claims and citations against course readings or credible sources.
- Judge: Label what is accurate, questionable, incomplete, biased, or unusable.
Have students submit an annotated response or a short explanation of what they would keep, revise, or reject.
Use a short scenario to have students decide whether and how to use AI.
Include the task, learning goal, information involved, and relevant course or professional expectations. Use it in a discussion, small-group activity, or low-stakes quiz.
Example
A healthcare student wants to paste a real patient’s clinical case into a public chatbot for an assignment that assesses clinical reasoning.
Ask three questions:
- Which risks matter here (accuracy, bias, privacy, ownership, and/or overreliance)?
- What should the student do?
- Which course or professional standard supports that decision?
Then change one condition and ask whether the decision changes. For example, make the patient fictional and remove identifying details.
Have students compare their answers and explain how the task, context, and consequences affected their judgment.
Require students to explain where and how AI affected their work. Encourage them to use disclosures that match the amount they used AI:
Minor use of AI
When to use: When AI provided minimal assistance to their process
Disclosure type: 1-2 sentence description
Example
I used [AI tool] to [specific purpose]. I reviewed the output and [describe what I changed, verified, or rejected].
Substantial use of AI
When to use: When AI shaped the analysis or final product
Disclosure type: short reflection or annotated record
Example
- When and why AI entered the process.
- What the tool contributed.
- How the student evaluated and verified the output.
- What the student accepted, revised, or rejected.
Use the disclosure to assess judgment: Was the purpose appropriate? Did the student verify the output? Did they remain responsible for the final choices?
Give feedback on one good decision the student made and one decision to improve next time.
Teach responsible AI use in three passes, reducing support each time:
- Model: Describe how you check an AI output, verify a claim, and decide what to keep.
- Guide: Give students clear boundaries for an assignment and provide feedback on their use of AI.
- Release: In a later assignment, ask students to decide whether AI belongs in the task and justify its role.
As students move through the three passes, shift your feedback from showing them what to do to asking them to explain their choices. This should help them progress from following guidance to exercising independent judgment.
Teach judgment that lasts
AI expectations should be treated like a continuous conversation. For each major assignment, describe what AI may do, what students must do themselves, and what they must disclose. Then invite students to raise gray areas, test examples, and explain how they would apply those expectations to the task. Use their questions and submitted work to continue the conversation and adjust future guidance as assignments and AI tools change. When students understand both the boundaries and the reasons behind them, they can use AI when it supports learning, leave it out when it would bypass learning, and carry that responsibility into future coursework, careers, and technologies that do not yet exist.
References
- Center for Teaching & Learning. (n.d.). Deepening students’ understanding of GenAI output. University of California, Berkeley. https://teaching.berkeley.edu/teaching-strategies/ai-teaching-learning/deepening-students%E2%80%99-understanding-genai-output
- Center for Teaching and Learning. (n.d.). Supporting students in using AI responsibly in your course. Columbia University. https://ai.ctl.columbia.edu/explore/responsible-student-use/
- Center for Teaching Innovation. (n.d.). AI attribution guidelines. Cornell University. https://teaching.cornell.edu/generative-artificial-intelligence/ai-attribution-guidelines
- Derek Bok Center for Teaching and Learning. (n.d.). AI literacy & ethics. Harvard University. https://bokcenter.harvard.edu/ai-literacy-and-ethics
- Digital Education Council. (2026). AI in higher education global survey 2026. https://26556596.fs1.hubspotusercontent-eu1.net/hubfs/26556596/DEC/Digital%20Education%20Council%20AI%20in%20Higher%20Education%20Global%20Survey%202026-1.pdf
- Furze, L., Perkins, M., Roe, J., & MacVaugh, J. (2024). The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI-supported assessment. Australasian Journal of Educational Technology, 40(4), 38–55. https://doi.org/10.14742/ajet.9434
- Liu, S., Lin, Z., Ng, O. L., Yang, Y., & Dai, Y. (2026). AI literacy interventions in education: A meta-analysis of effects and moderators. Educational Research Review, 52, Article 100813. https://doi.org/10.1016/j.edurev.2026.100813
- Marken, S. (2026, April 1). AI is routine for college students, despite campus limits. Gallup. https://news.gallup.com/poll/704090/routine-college-students-despite-campus-limits.aspx
- Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391105
- Teaching@UW. (n.d.). Centering learning when using AI with students. University of Washington. https://teaching.washington.edu/course-design/ai/student-learning/
- Weaver, K. D. (2024). The Artificial Intelligence Disclosure (AID) Framework: An introduction. College & Research Libraries News, 85(10), 407–411. https://doi.org/10.5860/crln.85.10.407
- Yan, W., Nakajima, T., & Sawada, R. (2025). Beyond tool use: Tracking the evolution of generative AI literacy among university students through a process-oriented investigation. Computers and Education: Artificial Intelligence, 9, Article 100465. https://doi.org/10.1016/j.caeai.2025.100465