Behavioral similarity between LLMs and learners does not guarantee mechanistic similarity¶

Claim · All claims
Evidence · 1 study · review r3 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Even when an LLM produces human-like answers or errors, it can be difficult to determine whether the underlying process resembles human cognition, the process-equivalence concern. → Peng Wang 2026

Evidence¶

Peng Wang 2026¶

Peng Wang, Olga Viberg, Effie Lai-Chong Law, Samuel Greiff. (2026). When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education. Educational Psychology Review. https://doi.org/10.1007/s10648-026-10202-y

q2 · i? · review · r3

Narrative review assertion about large language models as candidate cognitive models. The review explains that LLM knowledge is distributed across extremely large parameter spaces rather than localized in separable modules, so human-like output may match outcomes through different means than human cognition.

"This raises the process-equivalence concern (Firestone, 2020): behavioral similarity does not guarantee mechanistic similarity"

Discussion¶

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