Trust and reliance can diverge, and self-reported reliance measures cannot classify calibration without output-quality ground truth¶
Claim · All claims
Evidence · 1 study · reviewr2·q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
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.
q2 i? False ChatGPT information changed reported trust without measured acceptance or rejection of a specific recommendation, and reliance scales not tied to output correctness cannot classify reliance as calibrated. → Wei J and Shang Y 2026
Evidence¶
Wei J and Shang Y 2026¶
Wei J and Shang Y (2026). Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education. Front. Psychol. 17:1965371. https://doi.org/10.3389/fpsyg.2026.1965371
q2 · i? · review · r2
The review reports, citing Amaro et al. (2024), a user study showing false ChatGPT information changed reported trust without measuring subsequent acceptance or rejection. It adds that Hou et al.'s reliance scale "cannot by itself classify reliance as calibrated" because patterns are not tied to correct or incorrect AI suggestions.
"Amaro et al. (2024) showed that false ChatGPT information changed reported trust but did not measure subsequent acceptance or rejection of a specific recommendation."
Discussion¶
Related Claims¶
- Better awareness calibration co-occurs with lower incorrect-advice acceptance and lower attribution bias, interpreted associationally only — related
- Students frequently accepted misleading AI recommendations (overreliance) while rarely rejecting correct ones (underreliance) — related
- Viewpoint-aware spatial reasoning is a persistent bottleneck with poor calibration of spatial uncertainty — related
- Reflection improves correspondence between perceived and behavioral reliance on ChatGPT — related
- Reflection produced selective rather than blanket rejection of AI: alignment with correct advice stayed high while alignment with incorrect advice dropped — related
- A brief metacognitive reflection prompt reduces acceptance of incorrect AI advice compared with open ChatGPT support alone — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
trust-reliance-divergence-measures-not-calibrated |
title |
Trust and reliance can diverge, and self-reported reliance measures cannot classify calibration without output-quality ground truth |
description |
Trust and reliance can diverge, and self-reported reliance measures cannot classify calibration without output-quality ground truth |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — wei-j-and-shang-y-2026 |
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