A physics-education review reports one study (Ding et al. 2023) in which high-trust students agreed with ChatGPT's answers 100% of the time although those answers were 82% accurate¶
Claim · All claims
Evidence · 1 study · associationalr?·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? Ding et al. (2023) found ChatGPT achieved 85% overall accuracy across 362 questions, yet high-trust students agreed with responses 100% of the time despite 82% mean accuracy, with higher trust linked to more favorable perceptions (η2 = .257). → Malikova Z 2026
Evidence¶
Malikova Z 2026¶
Malikova Z, Ualikhanova B, Turmambekov T, Rakhashev B and Berdaliyev D (2026). Artificial intelligence in university physics education: a systematic review of empirical studies. Front. Educ. 11:1964629. https://doi.org/10.3389/feduc.2026.1964629
q2 · i? · the printed η2 = .257 is for trust and perceptions, not for agreement versus accuracy · associational · r?
The review reports this mixed-methods study of 40 undergraduates in Section 3.4; Table 5 lists ANOVA: F(2,37) = 6.383, p = .004, "η2 = .257" for the trust-related comparison, a large effect.
"Across 362 questions, ChatGPT achieved an overall accuracy of 85%. Among students in the high-trust group, the mean accuracy of ChatGPT responses was 82%, while participants agreed with the responses 100% of the time."
Discussion¶
Related Claims¶
- Reported outcomes of AI-supported university physics education are mixed and context-dependent, and the evidence does not support a conclusion that AI is uniformly effective — related
- Students did not consistently detect scientific errors in ChatGPT responses, and students with stronger prior knowledge evaluated AI responses more critically — related
- Reviewed studies consistently raise concerns about AI accuracy, overreliance, academic integrity, and reduced critical engagement in Arab EL2 contexts — related
- Error timing and stakes shape trust in AI systems: later errors may be more damaging, early errors may matter more, and trust recovers quickly after good post-error performance, with high-stakes errors causing abrupt collapse — related
- ChatGPT answered unsupervised web-based MCQ assessments with a mean accuracy of 70% (range 35–100%) — related
- General-purpose LLMs show accuracy and reliability limits, including hallucinations and task-specific accuracy below perfect, making them risky as expert advice sources — a broader claim this one bears on
- Before instruction, physics students show skepticism about LLM learning impacts, agreeing that over-reliance risks dependence, false confidence, and reduced conceptual understanding — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
trust-not-equivalent-to-ai-accuracy |
title |
A physics-education review reports one study (Ding et al. 2023) in which high-trust students agreed with ChatGPT's answers 100% of the time although those answers were 82% accurate |
description |
Students' trust in ChatGPT was not equivalent to its scientific accuracy: high-trust students agreed with responses 100% of the time despite 82% mean response accuracy |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — malikova-z-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.