The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows
The rapid integration of large language models (LLMs) into everyday workflows has transformed how individuals perform cognitive tasks such as writing, programming, analysis, and multilingual communication. While prior research has focused on model reliability, hallucination, and user trust calibration, less attention has been given to how LLM usage reshapes users' perceptions of their own capabilities. This paper introduces the LLM fallacy, a cognitive attribution error in which individuals misinterpret LLM-assisted outputs as evidence of their own independent competence, producing a systematic divergence between perceived and actual capability. We argue that the opacity, fluency, and low...
| Authors | Hyunwoo Kim, Harin Yu, Hanau Yi |
|---|---|
| Primary category | cs.AI |
| Published | 16 April 2026 |
| Last updated at source | 28 April 2026 |
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