LLM-As-An-Assessor: Can Open-Weight LLMs Assess Computational Thinking via Student-Designed Embodied Games?

William Lee, Sai Gattupalli, Ivon Arroyo, Brendan O’Connor


Abstract
We evaluate whether open-weight LLMs can assess Computational Thinking in middle-school students’ finite-state game designs against human labels. Across a rich and a sparse design, models detect some behaviors at near-human agreement but over-credit absent ones and fail at counting, loop detection, and standards mapping. Failures trace to fixable setup.
Anthology ID:
2026.aimecon-wip.37
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:
289–297
Language:
URL:
https://aclanthology.org/2026.aimecon-wip.37/
DOI:
Bibkey:
Cite (ACL):
William Lee, Sai Gattupalli, Ivon Arroyo, and Brendan O’Connor. 2026. LLM-As-An-Assessor: Can Open-Weight LLMs Assess Computational Thinking via Student-Designed Embodied Games?. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Works in Progress, pages 289–297, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
LLM-As-An-Assessor: Can Open-Weight LLMs Assess Computational Thinking via Student-Designed Embodied Games? (Lee et al., AIME-Con 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.aimecon-wip.37.pdf