Generative-Evaluative Agreement: A Necessary Validity Criterion for LLM-Enabled Adaptive Assessment

Grandee Lee; Yue Wang; Che Yee Lye; Luke Peh

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arXiv · 2605.19529

Generative-Evaluative Agreement: A Necessary Validity Criterion for LLM-Enabled Adaptive Assessment

Grandee Lee·Yue Wang·Che Yee Lye·Luke Peh

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Abstract

When the same LLM generates assessment items, simulates student responses, and scores them, the validation loop is self-referential. We introduce Generative-Evaluative Agreement (GEA), a validity criterion measuring whether an LLM's scoring function recovers the skill levels its generative function was instructed to produce. In the first direct measurement of GEA on a two-stage adaptive assessment, the model recovers roughly half the intended variance r = 0.698 with systematic positive bias. GEA is strong r > 0.7 for syntactically verifiable skills but near zero for design-level skills, and low-skill overestimation inflates scores near the routing threshold. We argue that granular, skill-decomposed rubrics are the principal proposed mechanism for strengthening GEA and outline complementary mitigations.

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Grandee Lee, Yue Wang, Che Yee Lye, Luke Peh. 2026-05-19. Generative-Evaluative Agreement: A Necessary Validity Criterion for LLM-Enabled Adaptive Assessment. https://arxiv.org/abs/2605.19529

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