Measuring Collaborative Reasoning with LLMs

Dawei Xie, Tochukwu Eze, Marcelo Worsley


Abstract
We present CRS, a framework representing group reasoning as contributions, relations, and derived structure. Evaluating five LLMs on student discussions, we find that reasoning is classifiable but not reliably segmentable, with identification being the bottleneck. Prompting improves labeling far more than identification; participation is recoverable, yet fine-grained structure is hard.
Anthology ID:
2026.aimecon-main.74
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
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:
656–665
Language:
URL:
https://aclanthology.org/2026.aimecon-main.74/
DOI:
Bibkey:
Cite (ACL):
Dawei Xie, Tochukwu Eze, and Marcelo Worsley. 2026. Measuring Collaborative Reasoning with LLMs. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 656–665, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Measuring Collaborative Reasoning with LLMs (Xie et al., AIME-Con 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.aimecon-main.74.pdf