Interpretable Humans, Alien LLMs: Expert Analysis of Latent Structures in Assessment Responses
The evaluation of large language models (LLMs) relies heavily on human-designed assessments, implicitly assuming that AI and humans employ similar underlying cognitive constructs. Challenging this assumption, we investigate whether the latent factors governing LLM performance carry the same substantive, human-interpretable meaning as the cognitive constructs governing human learners. Using responses from humans and six LLMs across quantitative reasoning and chemistry assessments, we conducted Exploratory Factor Analysis (EFA) separately for both groups. Subject-Matter Experts (SMEs) then blindly evaluated the resulting factor graphs to ascribe pedagogical meaning to the emerged constructs...
| Authors | Alona Strugatski, Licol Zeinfeld, Jason Cooper et al. |
|---|---|
| Primary category | cs.CL |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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