Talking to Learn: A SoTL Study of Generative AI-Facilitated Feynman Reviews

Madeline Rose Mattox, Natalie Hutchins, Jamie J Jirout


Abstract
Structured Generative AI interactions have potential for scaffolding learning. This Scholarship of Teaching and Learning study analyzes 16 undergraduate students’ Feynman-style AI interactions (N=157) across a semester-long child-development course. Qualitative coding of the interactions explores engagement patterns, metacognitive support, and response consistency, informing ethical AI integration in higher education.
Anthology ID:
2025.aimecon-wip.14
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:
October
Year:
2025
Address:
Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
Editors:
Joshua Wilson, Christopher Ormerod, Magdalen Beiting Parrish
Venue:
AIME-Con
SIG:
Publisher:
National Council on Measurement in Education (NCME)
Note:
Pages:
117–124
Language:
URL:
https://aclanthology.org/2025.aimecon-wip.14/
DOI:
Bibkey:
Cite (ACL):
Madeline Rose Mattox, Natalie Hutchins, and Jamie J Jirout. 2025. Talking to Learn: A SoTL Study of Generative AI-Facilitated Feynman Reviews. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 117–124, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Talking to Learn: A SoTL Study of Generative AI-Facilitated Feynman Reviews (Mattox et al., AIME-Con 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.aimecon-wip.14.pdf