The article proposes an empirical validation agenda: comparing perceived competence under LLM-assisted versus unaided conditions, with measurement frameworks and longitudinal designs¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The article argues the LLM fallacy should be operationalized as a measurable construct via metrics comparing self-assessed competence with independently evaluated performance, and via longitudinal studies of repeated LLM interaction. → Hyunwoo Kim 2026

Evidence¶

Hyunwoo Kim 2026¶

Hyunwoo Kim, Harin Yu, Hanau Yi. (2026). The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows. https://arxiv.org/abs/2604.14807 doi:10.48550/arxiv.2604.14807

q1 · i? · theoretical · r2

Future-work section proposing measurement frameworks for the LLM fallacy, including metrics comparing self-assessed competence with independently evaluated performance and task-specific benchmarks isolating LLM assistance. Conceptual proposal; "no effect size is reported" and no data are presented.

"Critically, these frameworks must capture both subjective perception and objective performance to accurately represent the gap."

Discussion¶

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