AI AI in Teaching and Learning

References

Every source cited on this site, grouped by topic. Older sources are labeled foundational or superseded. All links last reviewed 7 November 2026.

Recent research (Nov 2025 – Nov 2026)

  1. Ullah, E., & Kamel Boulos, M. N. (2026). Assessing generative artificial intelligence tools usage guidelines at the top 200 universities worldwide. Future Internet, 18(9), 492. doi.org/10.3390/fi18090492 ↗
  2. Crompton, H., Burke, D., Nickel, C., et al. (2026). Governing generative AI in higher education: A global Delphi study on policy and practice. International Journal of Educational Technology in Higher Education, 23, Article 21. doi.org/10.1186/s41239-026-00602-z ↗
  3. Chirikov, I. (2026). How instructors regulate AI in college: Evidence from 31,000 course syllabi. Center for Studies in Higher Education, UC Berkeley. cshe.berkeley.edu/news/new-study-31000-college-syllabi-shows-faculty-w… ↗
  4. Li, M., Xie, Q., Enkhtur, A., Meng, S., Chen, L., Yamamoto, B. A., Cheng, F., & Murakami, M. (2026). A framework for developing university policies on generative AI governance: A cross-national comparative study. Studies in Higher Education. tandfonline.com/doi/full/10.1080/03075079.2026.2696496 ↗
  5. Wu, E. (2026). Governing generative AI in digital education: How institutional guidance becomes course-level policy. Frontiers in Education, 11. frontiersin.org/journals/education/articles/10.3389/feduc.2026.1886876… ↗
  6. Sun, Møller, & Dohn. (2026). Governing generative AI in higher education: A cross-national analysis of policy framing and student maneuvering. Education Sciences, 16(10), 1704. doi.org/10.3390/educsci16101704 ↗
  7. Higher Education Policy Institute. (2026). Student generative AI survey 2026. hepi.ac.uk/reports/student-generative-ai-survey-2026/ ↗
  8. EDUCAUSE. (2026). The impact of AI on learning assessment. library.educause.edu/resources/2026/6/2026-educause-the-impact-of-ai-o… ↗
  9. EDUCAUSE. (2026). 2026 EDUCAUSE Horizon Report: Teaching and Learning Edition. library.educause.edu/resources/2026/5/2026-educause-horizon-report-tea… ↗
  10. Gu, X., Santo, W. L., Zumel Dumlao, J. M., & Ericson, B. (2026). To police or to guide: How higher education computer science instructors design and implement generative AI policies. arXiv:2607.16475. arxiv.org/abs/2607.16475 ↗
  11. From novelty to normalisation: Tracking changing perceptions of AI in higher education, 2024–2026. (2026). arXiv:2607.16223. arxiv.org/abs/2607.16223 ↗
  12. Roe, J., et al. (2026). Adapting the AI Assessment Scale for English for academic purposes. TESOL Journal. onlinelibrary.wiley.com/doi/full/10.1002/tesj.70122 ↗
  13. Contractor, Z., & Reyes, G. (2026). Experimental evidence on the learning impact of generative AI. arXiv:2607.08849; IZA Discussion Paper 18792. arxiv.org/abs/2607.08849 ↗
  14. Chen, X. (2026). Layer-sensitive cognitive offloading in generative AI-assisted writing: Supported performance and independent no-AI outcomes. Frontiers in Psychology, 17. doi.org/10.3389/fpsyg.2026.1906199 ↗
  15. Nakamura, D. (2026). Augmentation or erosion? Regulating cognitive offloading in generative AI-supported STEM learning. Educational Psychology Review, 38, Article 124. link.springer.com/article/10.1007/s10648-026-10224-6 ↗

AI detectors (Nov 2025 – Nov 2026)

  1. Van Vlasselaer, M., Van Droogenbroeck, F., & Spruyt, B. (2026). Who wrote this? Evaluating the reliability of AI detection tools in higher education. International Journal for Educational Integrity. (Correction published October 2026.) link.springer.com/article/10.1007/s40979-026-00226-w ↗
  2. Epoch AI. (2026, July). AI detectors rarely flag human writing, but sometimes miss AI text imitating real authors. epoch.ai/data-insights/ai-detectors-false-negatives ↗
  3. Why AI detection fails for academic integrity. (2026). arXiv preprint 2608.11256. arxiv.org/abs/2608.11256 ↗
  4. World Assessment Council. (2026). AI detectors can miss machines and accuse humans. Assessment has to change. worldassessmentcouncil.org/spotlight/ai-detectors-miss-machines-accuse… ↗
  5. Curtin University. Update on Turnitin AI-detection tool (effective 1 January 2026). curtin.edu.au/news/oasis-news/update-on-turnitin-ai-detection-tool/ ↗
  6. Dartmouth College, Faculty of Arts and Sciences. (2026, September). Guidelines on use of AI detection in teaching. fas.dartmouth.edu/sites/fas/files/2026-09/Faculty%20of%20Arts%20and%20… ↗
  7. The Dartmouth. (2026, October). Dean of faculty authorizes professors to use AI detector Pangram to review student work. thedartmouth.com/article/2026/10/hyde-dean-of-faculty-authorizes-profe… ↗
  8. Inside Higher Ed. (2026, August 5). AI detectors are out, new approaches are in. insidehighered.com/news/tech-innovation/artificial-intelligence/2026/0… ↗
  9. Financial Times. (2026). Coverage of universities dropping AI detection tools. ft.com/content/49304b1e-8a9d-4fb6-bc4d-37dd3430bb98 ↗
  10. Steptoe. (2026). AI and plagiarism in higher education: Guidance for education institutions. steptoe.com/en/news-publications/ai-and-plagiarism-in-higher-education… ↗

Sample syllabus statements (current institutional guidance)

  1. Carnegie Mellon University, Eberly Center. Examples of possible academic integrity policies that address student use of generative AI tools. cmu.edu/teaching/technology/aitools/academicintegrity/index.html ↗
  2. Harvard University FAS Office of Undergraduate Education. Generative AI guidance: draft course policies. oue.fas.harvard.edu/faculty-resources/generative-ai-guidance ↗
  3. Arizona State University, School of Life Sciences. Syllabus and policies for generative AI. tlc.sols.asu.edu/teaching/toolkits/syllabus-and-policies-generative-ai ↗
  4. University of Maryland, Teaching and Learning Transformation Center. Sample syllabus language: AI course policies. tltc.umd.edu/sample-syllabus-language-ai-course-policies ↗
  5. University of Texas at Austin, Faculty Innovation Center. ChatGPT and generative AI tools: Sample syllabus policy statements. fisd.utexas.edu/chatgpt-and-generative-ai-tools-sample-syllabus-policy… ↗
  6. NC State University, DELTA. Developing course AI guidelines. teaching-resources.delta.ncsu.edu/develop-an-ai-syllabus-statement/ ↗
  7. University of Wisconsin–Madison, Center for Teaching, Learning & Mentoring. AI statements for course syllabi. ctlm.wisc.edu/generativeai/ai-statements-for-course-syllabi/ ↗
  8. Tufts University, CELT. Developing syllabus statements for AI. provost.tufts.edu/celt/online-resources/artificial-intelligence/ai-syl… ↗
  9. UT San Antonio. Sample syllabus statements for student use of generative AI in coursework. utsa.screenstepslive.com/a/1904327-sample-syllabus-statements-for-stud… ↗
  10. Eaton, L. Syllabi policies for AI generative tools [Crowdsourced repository]. bit.ly/AI-Syllabi ↗

Institutional guidance & frameworks

  1. Campus AI Registry & Governance Framework: Documentation hierarchy. campusairegistry.com/hierarchy ↗
  2. Campus AI Registry & Governance Framework: Artifacts & relationships. campusairegistry.com/artifacts ↗
  3. Campus AI Registry & Governance Framework: Publication review. campusairegistry.com/publication ↗
  4. Stanford University, Office of Community Standards. Generative AI policy guidance. communitystandards.stanford.edu/generative-ai-policy-guidance ↗
  5. Harvard University Information Technology. Guidelines for using generative AI tools. huit.harvard.edu/ai/guidelines ↗
  6. AI Assessment Scale (Perkins, Furze, Roe & MacVaugh). aiassessmentscale.com/ ↗

Foundational evidence and frameworks (before Nov 2025, still applicable)

  1. Cornell University. (2023). CU committee report: Generative artificial intelligence for education and pedagogy. teaching.cornell.edu/generative-artificial-intelligence/cu-committee-r… ↗
  2. MLA-CCCC Joint Task Force on Writing and AI. (2024). Working papers. aiandwriting.hcommons.org/working-papers/ ↗
  3. UNESCO. (2023). Guidance for generative AI in education and research. unesco.org/en/articles/guidance-generative-ai-education-and-research ↗
  4. Russell Group. (2023). Principles on the use of generative AI tools in education. russellgroup.ac.uk/media/6137/rg_ai_principles-final.pdf ↗
  5. Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20, 38. doi.org/10.1186/s41239-023-00408-3 ↗
  6. Furze, L., Perkins, M., Roe, J., & MacVaugh, J. (2024). The AI Assessment Scale in action: A pilot implementation. arXiv:2403.14692. doi.org/10.48550/arXiv.2403.14692 ↗
  7. An, Y., Yu, J. H., & James, S. (2025). Investigating the higher education institutions’ guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration. International Journal of Educational Technology in Higher Education. link.springer.com/article/10.1186/s41239-025-00507-3 ↗
  8. McDonald, N., Johri, A., Ali, A., & Collier, A. H. (2025). Generative artificial intelligence in higher education: Evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans. doi.org/10.1016/j.chbah.2025.100121 ↗

Superseded in part or historical

  1. Liang, W., et al. (2023). GPT detectors are biased against non-native English writers. Patterns, 4. doi.org/10.1016/j.patter.2023.100779 ↗