Generative AI in Assessment: Approaches for Your Courses
Generative AI is now part of everyday teaching and learning. This guide helps instructors and teaching teams make clear, intentional decisions about assessment, integrity, feedback, privacy, and equity.
Authors: Lorelei Anselmo, MEd & Ali Mikaeili, EdD (Student)
*Adapted from the original resource developed by: Lorelei Anselmo, MEd & Tyson Kendon, PhD, Beatriz Moya, PhD
Introduction / Context
Recent developments in artificial intelligence tools, including ChatGPT, have generated a wide range of responses in higher education, from concerns about academic integrity to opportunities for innovation. Navigating these developments as part of teaching and learning remains critical (Eaton, 2022).
Instructors are now working within a broader generative AI ecosystem that includes text, image, audio, multimodal, and increasingly agentic tools. This resource offers suggestions for instructors and teaching teams considering how generative AI may affect assessment, student work, feedback, communication, privacy, and academic integrity. Generative AI continues to transform how assessment is designed and experienced in higher education (Xia et al., 2024).
Where to start
Addressing generative AI in assessment requires building trust, collaborating with colleagues, connecting with teaching teams, and communicating clearly with students.
Working with your teaching team
How could Generative AI support assessment work responsibly?
Generative AI is not a replacement for student work, teaching-team judgment, or authentic assessment. Used carefully, it may support some assessment-related tasks, such as improving assignment instructions, drafting sample feedback, creating practice questions, or generating examples for students to critique.
All AI-generated material should be reviewed before being used with students. AI-generated comments may be generic, inaccurate, or misaligned with course expectations. This is especially important for outputs such as translations or summaries, which can introduce errors or omit key context.
Instructors should not enter identifiable student information, graded student work, private course data, unpublished research, or confidential assessment material into public AI tools unless the tool has been institutionally approved and appropriate safeguards are in place. If a tool will be required for students, consult institutional IT, privacy, or information security guidance first. For related guidance on privacy and appropriate AI use, see the companion resource, Exploring Artificial Intelligence in Teaching and Learning.
What are your expectations?
As a teaching team, identify the role AI may play in each assessment. A simple structure can help:
- AI-free: when the assessment is intended to measure unaided individual performance.
- AI scaffolded: Allow AI use with disclosure: when AI may support part of the learning process, such as brainstorming, editing, or revision.
- AI integrated: Encourage AI use for a specific purpose: when the learning outcome includes evaluating, revising, or critiquing AI-generated output.
AI use is widespread yet still varies widely among students (Chung et al., 2026). As a result, academic integrity questions surrounding AI use are complex (Eaton, 2022). Whether AI use is appropriate depends on the assignment instructions, the learning outcomes, the disclosure expectations, and whether AI replaced the thinking students were expected to demonstrate.
AI detection and red flags
It is not possible to determine with certainty whether writing was generated by AI based only on style, tone, or generic language. AI detection tools are imperfect and may produce false positives that wrongly implicate students, or miss AI-generated content altogether (Perkins et al., 2023).
Clear instructions, assessment design, student documentation, and conversations about learning are stronger starting points.
Using AI to support feedback
AI may help teaching teams draft clearer comments, expand short notes, or generate examples of feedback language. Instructors should use anonymized or fictionalized examples when experimenting with feedback prompts and should review all AI-generated feedback before sharing it with students.
AI should not be used to make autonomous consequential grading decisions.
Example
A teaching team might use AI to help turn brief, anonymized feedback notes into clearer student-facing comments. The teaching team would then review, revise, and approve the feedback before sending it to students.
Protect privacy and student data
Information entered into public AI tools may be stored, reused, or exposed (Farrelly & Baker, 2023). Do not enter identifiable student information, graded work, unpublished student work, or confidential course material into public tools unless the tool is institutionally approved with safeguards in place. This applies not only to confidential information, but to other student-created intellectual property, which should not be shared without the creator’s permission. At the University of Calgary, privacy and access obligations now fall under Alberta’s Protection of Privacy Act (POPA; https://www.alberta.ca/protection-of-privacy-act) and Access to Information Act (ATIA), which replaced FOIP on June 11, 2025.
Related content
Articles and resources for ChatGPT
Additional readings
Castelvecchi. D. (2022, December 8). Are ChatGPT and AlphaCode going to replace programmers? Nature (London). https://www-nature-com.ezproxy.lib.ucalgary.ca/articles/d41586-022-04383-z
CESE NSW What Works Best in Practice. (n.d.) A Teacher’s Prompt Guide to ChatGPT. https://usergeneratededucation.files.wordpress.com/2023/01/a-teachers-prompt-guide-to-chatgpt-aligned-with-what-works-best.pdf
Eaton, S. (2022). Sarah’s thoughts: Artificial intelligence and academic integrity. Learning, Teaching and Leadership: A blog for educators, researchers and other thinkers by Sarah Elaine Eaton, PhD. https://drsaraheaton.wordpress.com/2022/12/09/sarahs-thoughts-artificial-intelligence-and-academic-integrity/
Eaton, S., & Anselmo, L. (2023, January 12). Teaching and Learning with Artificial Intelligence Apps. Taylor Institute for Teaching and Learning, University of Calgary. https://taylorinstitute.ucalgary.ca/teaching-with-AI-apps
Gordijn, B. & Have, H. T. (2023). ChatGPT: evolution or revolution? Medicine, Health Care, and Philosophy. 1-2. https://doi.org/10.1007/s11019-023-10136-0
Hemsley, B., Power, E., & Given, G. (2023, January 18). Will AI tech like ChatGPT improve inclusion for people with communication disability? The Conversation. https://theconversation.com/will-ai-tech-like-chatgpt-improve-inclusion-for-people-with-communication-disability-196481
Kumar, R., Mindzak, M., Eaton, S. E., & Morrison, R. (May 17, 2022). AI & AI: Exploring the contemporary intersections of artificial intelligence and academic integrity [Conference Session]. Canadian Society for the Study of Higher Education Annual Conference [Online]. http://hdl.handle.net/1880/114647
Mills, A. (2023). What to do about artificial intelligence text generators. [Webinar]. College of Marin. https://docs.google.com/presentation/d/1P5nSOm1g3CvsoPEga4fSnzjMiLtGg7Bf/mobilepresent?slide=id.p2
Mollick, Ethan R. and Mollick, Lilach, New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments (December 13, 2022). Available at SSRN: https://ssrn.com/abstract=4300783 or http://dx.doi.org/10.2139/ssrn.4300783
Taylor, J. (2023, February 1) ChatGPT maker OpenAI releases ‘not fully reliable’ tool to detect AI generated content The Guardian. https://www.theguardian.com/technology/2023/feb/01/chatgpt-maker-openai-releases-ai-generated-content-detection-tool
Working with your students
How can generative AI affect learning and teaching?
Generative AI can support learning when students use it to ask questions, explore ideas, or critique outputs, and it can get in the way when it replaces the thinking a task is meant to develop. Build students’ judgment about when AI helps and when it does not, and expect them to verify what it produces (Sardi et al., 2025).
What are your expectations?
Discuss generative AI openly, including how it affects students’ work and your assessment of it. Co-create guidelines where it makes sense, and document them clearly in assignment instructions, rubrics, or course policy (Eaton & Anselmo, 2023).
- Name the limits. Help students recognize hallucinations, fabricated citations, bias, and missing course context (Kasneci et al., 2023; Chan & Hu, 2023).
Ask for verification. Students should check AI output against course materials and document what they used, why, and what they changed.
Example Have students generate an AI response, compare it to a rubric, flag errors, bias, or missing context, revise it, and reflect on what they learned. The critique becomes the assignment.
Example Agree on a simple way to document AI use: tool, purpose, prompt, what was kept, and what the student changed, and indicate it in your rubric.
Example
Have students generate an AI response, compare it to a rubric, flag errors, bias, or missing context, revise it, and reflect on what they learned. The critique becomes the assignment.
Example
Agree on a simple way to document AI use: tool, purpose, prompt, what was kept, and what the student changed, and indicate it in your rubric.
Accessibility and equity
AI can support learning through translation, text simplification, and language support. At the same time, weigh cost and access to paid tools, and offer alternatives for students who cannot or prefer not to use a particular tool. Students have free access to Microsoft Copilot Chat, the University of Calgary’s institutionally-supported AI tool, which can help address some of these cost and access concerns.
Design assessment to make thinking visible
Process-rich tasks reduce the payoff of outsourcing work to AI: staged submissions, drafts and revision notes, oral follow-ups, individualized or local cases, and assignments that ask students to critique AI output (Moorhouse et al., 2023; Qian, 2025).
Reflect
The capabilities of AI tools and responses in higher education continue to change. Use this reflection to inform future assessment design, communication, and course policies.
- Were expectations clear? This helps identify whether students understood the AI-use policy.
- How did students actually use AI, and did disclosure work? This helps instructors understand actual practices, not just expected use.
- What questions or confusion came up? This shows where instructions may need revision.
- Were there privacy, access, or equity concerns? This identifies barriers or unintended consequences that may need alternatives.
- Did the assessment still measure the intended learning outcomes? This keeps assessment design aligned with learning.
- What should change next time? This supports ongoing improvement of assessment design and communication.
UCalgary’s Principles for Assessment of Student Learning (keep this as previously)
References
Chan, C., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1), Article 43. https://doi.org/10.1186/s41239-023-00411-8
Chung, J., Henderson, M., Slade, C., Liang, Y., Pepperell, N., Corbin, T., Yu, A. S., Bearman, M., Buckingham Shum, S., Fawns, T., McCluskey, T., McLean, J., Oberg, G., Seligmann, A., Shibani, A., Bakharia, A., Lim, L. A., & Matthews, K. E. (2026). The use and usefulness of GenAI in higher education: Student experience and perspectives. Computers and Education Open, Article 100347. https://www.sciencedirect.com/science/article/pii/S2666557326000182
Dabis, A., & Csáki, C. (2024). AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI. Humanities and Social Sciences Communications, 11, Article 1006. https://doi.org/10.1057/s41599-024-03526-z
Eaton, S. (2022). Sarah’s thoughts: Artificial intelligence and academic integrity. Learning, Teaching and Leadership. https://drsaraheaton.wordpress.com/2022/12/09/sarahs-thoughts-artificial-intelligence-and-academic-integrity/
Eaton, S., & Anselmo, L. (2023, January 12). Teaching and learning with artificial intelligence apps. Taylor Institute for Teaching and Learning, University of Calgary. https://taylorinstitute.ucalgary.ca/teaching-with-AI-apps
Farrelly, T., & Baker, N. (2023). Generative artificial intelligence: Implications and considerations for higher education practice. Education Sciences, 13(11), Article 1109. https://doi.org/10.3390/educsci13111109
Hemsley, B., Power, E., & Given, F. (2023, January 18). Will AI tech like ChatGPT improve inclusion for people with communication disability? The Conversation. https://theconversation.com/will-ai-tech-like-chatgpt-improve-inclusion-for-people-with-communication-disability-196481
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Luo (Jess), J. (2025). How does GenAI affect trust in teacher–student relationships? Insights from students’ assessment experiences. Teaching in Higher Education, 30(4), 991–1006. https://doi.org/10.1080/13562517.2024.2341005
Moorhouse, B. L., Yeo, M. A., & Wan, Y. (2023). Generative AI tools and assessment: Guidelines of the world’s top-ranking universities. Computers and Education Open, 5, Article 100151. https://doi.org/10.1016/j.caeo.2023.100151
Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2023). Detection of GPT-4 generated text in higher education: Combining academic judgement and software to identify generative AI tool misuse. Journal of Academic Ethics, 22, 89–113. https://doi.org/10.1007/s10805-023-09492-6
Qian, Y. (2025). Pedagogical applications of generative AI in higher education: A systematic review of the field. TechTrends, 69, 1105–1120. https://doi.org/10.1007/s11528-025-01100-1
Sardi, J., Darmansyah, Candra, O., Yuliana, D. F., Habibullah, Yanto, D. T. P., & Eliza, F. (2025). How generative AI influences students’ self-regulated learning and critical thinking skills: A systematic review. International Journal of Engineering Pedagogy, 15(1), 94–108. https://doi.org/10.3991/ijep.v15i1.53379
Tong, S. T., DeTone, A., Frederick, A., & Odebiyi, S. (2025). What are we telling our students about AI? An exploratory analysis of university instructors’ generative AI syllabi policies. Communication Education, 74(3), 261–282. https://doi.org/10.1080/03634523.2025.2477479
Xia, Q., Weng, X., Fan, O., Lin, T.-J., & Chiu, T. K. F. (2024). How generative AI is transforming assessment in higher education: A scoping review. International Journal of Educational Technology in Higher Education, 21, Article 40. https://doi.org/10.1186/s41239-024-00468-z