Dr. Laura Dumin
Dr. Laura Dumin was our guest on the podcast "What Training and Development Can Learn from AI in Higher Education". You can learn more about her work and follow her social media on her website.
Metacognition 2.0: Teaching Learners to Think About How AI Thinks
Hurley, K. (2025). The paradox of AI assistance: Better results, worse thinking. EDUCAUSE Review. https://er.educause.edu/articles/2025/12/the-paradox-of-ai-assistance-better-results-worse-thinking
Kalai, A. T., Nachum, O., Vempala, S. S., & Zhang, E. (2025). Why language models hallucinate. arXiv:2509.04664. https://doi.org/10.48550/arXiv.2509.04664
Naiseh, M., Al-Thani, D., Jiang, N., & Ali, R. (2023). How the different explanation classes impact trust calibration: The case of clinical decision support systems. International Journal of Human-Computer Studies, 169, 102941. https://doi.org/10.1016/j.ijhcs.2022.102941
Lee, D., Pruitt, J., Zhou, T., Du, J., & Odegaard, B. (2025). Metacognitive sensitivity: The key to calibrating trust and optimal decision making with AI. PNAS Nexus, 4(5), pgaf133. https://doi.org/10.1093/pnasnexus/pgaf133
Brains on Borrowed Time: What Students Lose — and Gain — When AI Thinks for Them
Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Georgiou, G. P. (2025). ChatGPT produces more ‘lazy’ thinkers: Evidence of cognitive engagement decline. University of Nicosia.
Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., S, M., & Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in psychology, 16, 1550621.
Kosmyna, N., et al. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task. arXiv:2506.08872.
Bjork, R. A., & Bjork, E. L. (2020). Desirable difficulties in theory and practice. Journal of Applied Research in Memory and Cognition, 9(4), 475–479.
Fischer, M., Rau, H. A., & Rilke, R. M. (2025). AI tutoring enhances student learning without crowding out reading effort. IZA Institute of Labor Economics, 18338.
Burns, M. (2026, January 27). What the research shows about generative AI in tutoring. Brookings. https://www.brookings.edu/articles/what-the-research-shows-about-generative-ai-in-tutoring.
Torres, P. J., & Kahveci, Y. E. (2025). Effectiveness of Artificial Intelligence (AI) in language teaching. Computers and Education: Artificial Intelligence, 100522.
Trust But Verify Redefining Academic Integrity in an AI Enabled Classroom
Lumina Foundation-Gallup. (2026, April 2). AI in higher education: Widespread use, unclear rules. Gallup. https://www.gallup.com/analytics/644939/state-of-higher-education.aspx
Oldham, C. (2025). Artificial intelligence and assessment: Are universities ready to rethink integrity? ICAI. https://academicintegrity.org
Weber-Wulff, D., et al. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), 26. https://doi.org/10.1007/s40979-023-00146-z
Dawson, P. (2020). Cognitive offloading and assessment. In M. Bearman, P. Dawson, R. Ajjawi, J. Tai, & D. Boud (Eds.), Re-imagining university assessment in a digital world (pp. 37–48). Springer. https://doi.org/10.1007/978-3-030-41956-1_4
Lee, D., Pruitt, J., Zhou, T., Du, J., & Odegaard, B. (2025). Metacognitive sensitivity: The key to calibrating trust and optimal decision making with AI. PNAS Nexus, 4(5), pgaf133. https://doi.org/10.1093/pnasnexus/pgaf133
Hurley, K. (2025). The paradox of AI assistance: Better results, worse thinking. EDUCAUSE Review. https://er.educause.edu/articles/2025/12/the-paradox-of-ai-assistance-better-results-worse-thinking
Beyond the Red Pen: Designing AI Feedback Loops That Actually Teach
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. Assessment in Education: Principles, Policy & Practice, 5(1), 7–74.
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218.
Carless, D., & Boud, D. (2018). The development of student feedback literacy: Enabling uptake of feedback. Assessment & Evaluation in Higher Education, 43(8), 1315–1325.
Carless, D. (2019). Feedback loops and the longer-term: Towards feedback spirals. Assessment & Evaluation in Higher Education, 44(5), 705-714.
Henderson, M., Phillips, M., Ryan, T., Boud, D., Dawson, P., Molloy, E., & Mahoney, P. (2019). Conditions that enable effective feedback. Higher Education Research & Development, 38(7), 1401-1416.
Mpolomoka, D. L. (2025). Utilizing Artificial Intelligence for Assessment in Higher Education. Pedagogical Research, 10(3).
Wisniewski, B., Zierer, K., & Hattie, J. (2020). The power of feedback revisited: A meta-analysis of educational feedback research. Frontiers in Psychology, 10, 3087.
Tensen, D., Grainger, P., & Graham, W. (2026). Using AI to generate formative feedback in doctoral education. Assessment & Evaluation in Higher Education, 51(3), 476-492.
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 16(1).