Classroom AI Policies: Difference between revisions
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== Resources == | == Resources == | ||
*[[File:Classroom-AI-Policies.pdf Classroom AI Policies Slidedeck (PDF)]] | *[[File:Classroom-AI-Policies.pdf| Classroom AI Policies Slidedeck (PDF)]] | ||
== References == | == References == | ||
Revision as of 11:51, 30 January 2026
''A workshop titled "Mini-Workshop: Classroom AI Policies" originally presented for CAT+FD on 27 January 2026 by Dr. Jason Todd
Abstract
Developing effective classroom AI policies requires moving beyond a simple binary of permission or restriction to a nuanced framework that considers specific course goals, institutional context, and the enhancement of student learning. Because a student’s personal ethical framework often influences their behavior more than mere awareness of rules, policies should prioritize educational transparency by clearly explaining the "why" behind boundaries and connecting them to disciplinary norms and learning outcomes. Instructors are encouraged to tailor policies for individual classes rather than applying a universal standard, acknowledging that AI can range from an academic integrity violation to a powerful research and editing tool depending on the assignment's purpose. As technology and institutional support systems evolve, maintaining an ongoing dialogue with students about ethical use and professional relevance ensures that these policies remain flexible and effective in fostering meaningful learning.
Resources
References
- Alsharefeen, R., & Sayari, N. A. (2025). Examining academic integrity policy and practice in the era of AI: a case study of faculty perspectives. Frontiers in Education. https://doi.org/10.3389/feduc.2025.1621743
- Deep, P. D., Edgington, W. D., Ghosh, N., & Rahaman, Md. S. (2025). Evaluating the Effectiveness and Ethical Implications of AI Detection Tools in Higher Education. Information, 16(10), 905. https://doi.org/10.3390/info16100905
- Gustilo, L., Ong, E., & Lapinid, M. R. (2024). Algorithmically-driven writing and academic integrity: exploring educators’ practices, perceptions, and policies in AI era. International Journal for Educational Integrity. https://doi.org/10.1007/s40979-024-00153-8
- Hamerman, E. J., Aggarwal, A., & Martins, C. M. (2024). An investigation of generative AI in the classroom and its implications for university policy. Quality Assurance in Education. https://doi.org/10.1108/qae-08-2024-0149
- Kangwa, D., Msafiri, M. M., & Fute, A. (2025). Exploring the Factors That Promote a Balance Between Academic Integrity and the Effective Use of GenAI Tools in Higher Education: A Systematic Review. Journal of Computer Assisted Learning, 41(5). https://doi.org/10.1111/jcal.70109
- Lund, B., Mannuru, N. R., Teel, Z. A., Lee, T. H., Ortega, N. J., Simmons, S., & Ward, E. (2025). Student Perceptions of AI-Assisted Writing and Academic Integrity: Ethical Concerns, Academic Misconduct, and Use of Generative AI in Higher Education. AI in Education, 1(1), 2. https://doi.org/10.3390/aieduc1010002
- Plata, S., De Guzman, M. A., & Quesada, A. (2023). Emerging Research and Policy Themes on Academic Integrity in the Age of Chat GPT and Generative AI. Asian Journal of University Education, 19(4), 743–758. https://doi.org/10.24191/ajue.v19i4.24697
- Streletska, N., Ulishchenko, A., Klieba, A., Vlasiuk, I., & Genkal, S. (2024). Integrating artificial intelligence into STEM education: Navigating academic integrity. Multidisciplinary Reviews, 8, 2024spe069. https://doi.org/10.31893/multirev.2024spe069
- Sumilong, M. J. (2025). Instructional affect and learner motivation in generative AI-restrictive and permissive classrooms. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1626802
- Xie, Y., Chen, X., Ren, Z., & Su, W. J. (2025). Watermark in the Classroom: A Conformal Framework for Adaptive AI Usage Detection.