ChatGPT 3.5 produced low-quality hints for 32% of problems, reducible to near 0% for algebra and 13% for statistics via the self-consistency technique¶
Claim · All claims
Evidence · 1 study · causalr2·q3
Currency · newest study 2024 · not yet checked against its sources · not reviewed by a person · recheck by 2027: 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.
q3 i? Self-consistency reduced error to 0% (Elementary Algebra), 2% (Intermediate Algebra), 2% (College Algebra), and 13% (Statistics). → Pardos ZA 2024
Evidence¶
Pardos ZA 2024¶
Pardos ZA, Bhandari S (2024) ChatGPT-generated help produces learning gains equivalent to human tutor-authored help on mathematics skills. PLoS ONE 19(5): e0304013. https://doi.org/10.1371/journal.pone.0304013
q3 · i? · causal · r2
Application of self-consistency (10 samples per question, modal answer kept) evaluated by the same rater procedure. The article concludes "self-consistency helps reduce ChatGPT's error rate, with algebra topics reaching close to 0% error rates"; no effect size is printed.
"This approach, called self-consistency and detailed in the Methods section, resulted in an Elementary Algebra error of 0%, Intermediate Algebra error of 2%, College Algebra error of 2%, and Statistics error of 13%."
Discussion¶
Related Claims¶
-
Learners receiving ChatGPT-generated hints showed statistically significant learning gains compared to a no-hint control in an online mathematics tutoring experiment — the general claim this finding bears on, with the studies behind it
-
Contingent scaffolding improves learning more than fixed or absent support. — the general claim this finding bears on, with the studies behind it
- Learning gains from ChatGPT-generated hints were not statistically significantly different from human tutor-authored hints, though ChatGPT gains were numerically higher in all subjects — related
- Different learner contexts supplied to an AI tutor produce qualitatively different, persona-aligned explanations of the same problem — related
- GPT-4 generates targeted corrective feedback for 66% of student errors, but 35% of hints are too general, incorrect, or give away the answer — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
chatgpt-hint-error-rate-self-consistency |
title |
ChatGPT 3.5 produced low-quality hints for 32% of problems, reducible to near 0% for algebra and 13% for statistics via the self-consistency technique |
description |
ChatGPT 3.5 produced low-quality hints for 32% of problems, reducible to near 0% for algebra and 13% for statistics via the self-consistency technique |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2024 |
recheck_by |
2027 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — pardos-za-2024 |
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