Which AI Assessment Judgments Should Educators Review First?

Under review. An offline comparison of rules for prioritizing human review of AI grading judgments, evaluating potential over-credit and missed ideas separately.

Authors: Iman YeckehZaare, Louwis Truong, Dahe Wang, Jayson Malasig, Sahana Iyer, Biyang Zhang, Abdessamad Ouhra Ali · Year: 2026 · Submitted to: LAK 2027 · Research Full Papers · Submission updated: 2026-09-29

This submitted manuscript compares review-prioritization rules across fourteen model configurations and 2,823 student response–idea cases. It evaluates potential over-credit and missed ideas separately, including sensitivity to researcher-reference uncertainty. Educator correction and learning outcomes remain untested.

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