MIST: Misconception Identification and Sycophancy Tracking

Lionel Meng, Lingbo Tong, Icy Zhang


Abstract
This study investigates effective use of AI models as statistics tutors. Specifically, how to promote accurate student misconception identification, elicit active student cognition, and provide trustworthy responses. Initial findings from explorations of prompting strategies and indicators of sycophantic behavior with emphasis on model reasoning traces in simulated conversations are discussed.
Anthology ID:
2026.aimecon-wip.43
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress
Month:
October
Year:
2026
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:
335–343
Language:
URL:
https://aclanthology.org/2026.aimecon-wip.43/
DOI:
Bibkey:
Cite (ACL):
Lionel Meng, Lingbo Tong, and Icy Zhang. 2026. MIST: Misconception Identification and Sycophancy Tracking. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 335–343, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
MIST: Misconception Identification and Sycophancy Tracking (Meng et al., AIME-Con 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.aimecon-wip.43.pdf