Automation bias toward fluent LLM output surrenders learners' epistemic agency¶

Claim · All claims
Evidence · 1 study · theoretical r2 · q1
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q1 i? Automation bias leads learners to stop feeling responsible for verifying the truth of AI-generated output, a surrender of epistemic agency. → Wang H and Shan W 2026

Evidence¶

Wang H and Shan W 2026¶

Wang H and Shan W (2026). The safety gap: restoring productive struggle through pedagogically aligned generative AI. Front. Educ. 11:1757622. https://doi.org/10.3389/feduc.2026.1757622

q1 · i? · theoretical · r2

Theoretical argument in the article's section on automation bias and the fluency heuristic: the authors argue that confident, fluent LLM output triggers a coherence heuristic in learners, and that 'the learner stops feeling responsible for verifying the truth of the output'. No empirical data are reported for this claim.

"This leads to a surrender of epistemic agency, where the learner stops feeling responsible for verifying the truth of the output."

Discussion¶

- ChatGPT users differentiated information less (never by credibility) and rarely verified information compared to Google users — the general claim this finding bears on, with the studies behind it¶

Productive cognitive offloading to AI can shift into cognitive surrender, abdicating learner intellectual agency — a broader claim this one bears on
Distributed epistemic agency emerges as learners attribute authority to AI outputs and shift responsibility for knowledge validation, though some engage dialogically — possibly the same claim (merge candidate)
AI intuition emphasizes epistemic judgment rather than output optimization, distinguishing it from prompt engineering — related
Providing LLM pre-labels can decrease human annotation accuracy when the model is incorrect, due to automation bias — related

Page metadata

Field	Value
`type`	`claim`
`id`	`automation-bias-surrenders-epistemic-agency`
`title`	`Automation bias toward fluent LLM output surrenders learners' epistemic agency`
`description`	`Automation bias toward fluent LLM output surrenders learners' epistemic agency`
`status`	`review`
`evidence_strength`	`weak`
`newest_evidence`	`2026`
`source_checked`	`2026-10-20`
`generated`	by `process:wiki-ingest` on `2026-10-12`
`trust`	unverified — no one has confirmed this page's content
`sources`	1 — `wang-h-and-shan-w-2026`

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