No trace format supported calibrated self-evaluation: perceived-achieved correlations were low and non-significant in all conditions¶
Claim · All claims
Evidence · 1 study · causalr2·q3
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.
q3 i? AUROC2 did not differ across conditions, and the confidence-gap (discrimination) effect size did not differ across conditions. → Fernandes 2026
Evidence¶
Fernandes 2026¶
Fernandes, D., Buschek, D., Tankelevitch, L., Kosch, T., & Welsch, R. (2026). Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition. https://arxiv.org/abs/2605.25856 doi:10.48550/arxiv.2605.25856
q3 · i? · causal · r2
Trial-level Type-2 ROC area computed per participant from ten items did not differ across conditions (F(2,555)=0.97, p=.380); the confidence-gap ANOVA likewise showed no condition difference (F(2,555)=0.84, p=.432).
"Mean AUROC2 was nearly identical across conditions (Answer-only:𝑀= 0.586,𝑆𝐷= 0.206;Full-trace: 𝑀= 0.562,𝑆𝐷= 0.192;Summary-trace: 𝑀= 0.587,𝑆𝐷=0.202) and conditions did not differ,𝐹(2,555)=0.97,𝑝=.380,𝜂 2 =.003."
Discussion¶
Learner Variables¶
- Metacognition — outcome: instruction changes it
Related Claims¶
- Self Assessment Accuracy Is Low Without Training — the general claim this finding bears on, with the studies behind it
- Full reasoning traces impaired task performance relative to both answer-only and summary-trace conditions in AI-assisted LSAT reasoning — related
- Full reasoning traces worsened calibration bias and Brier scores relative to both other conditions — related
- Guided-LLM students showed the strongest self-assessment calibration, with the highest well-calibrated rate and strongest self-assessment–performance correlations — related
- Review-attributed evidence: user confidence in model answers exceeds model internal confidence, and explanation design narrows the gap — related
- Tell and no-agent conditions showed no significant JoL–confidence correlations, with mixed results for progress and mastery — related
- Participants substantially overestimated their performance in all trace conditions, with the largest overestimation under full traces — related
- Summary traces preserved task performance at the answer-only baseline while elevating subjective appraisal — related
- Children's self-focused confidence reasoning and other-focused accuracy reasoning show no consistent correlation across four studies when mental-state language and response biases are eliminated
- The tribute reports students tend to overestimate their sport performance, and four-level model instruction may improve calibration accuracy — related
- Reasoning-trace exposure raised trust and hedonic appeal without performance benefits, shifting subjective appraisal of the interaction — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
no-trace-format-supported-calibrated-self-evaluation |
title |
No trace format supported calibrated self-evaluation: perceived-achieved correlations were low and non-significant in all conditions |
description |
No trace format supported calibrated self-evaluation: perceived-achieved correlations were low and non-significant in all conditions |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — fernandes-2026 |
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