Project performance improved significantly from pre- to post-feedback across all three feedback conditions with no significant differences between feedback sources¶
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? Students' project performance improved significantly from pre- to post-feedback in all conditions (rrb = 0.77), with no statistically significant differences between AI and human feedback sources; equivalence was tested between GPT-o4-mini and teacher feedback, and non-inferiority for DeepSeek R1. → Grion 2026
q3 i? GPT-o4-mini feedback was practically equivalent to teacher feedback, and DeepSeek R1 feedback was non-inferior to teacher feedback in effects on project performance. → Grion 2026
Evidence¶
Grion 2026¶
Grion, Valentina; Doria, Beatrice; Agostini, Daniele; Slaviero, Giorgia. (2026). Artificial intelligence and feedback in university education: effectiveness and student perceptions. Assessment and Evaluation in Higher Education. https://dx.doi.org/10.1080/02602938.2026.2697962
q3 · i? · causal · r2
Quasi-experimental study: 47 student groups (N = 238) randomly assigned to GPT-o4-mini, DeepSeek R1, or expert teacher feedback conditions; changes analysed with non-parametric tests, robust models, and non-inferiority and equivalence analyses. The article reports "significant improvement in project performance" with rrb = 0.77.
"Results showed significant improvement in project performance from pre- to post-feedback across all conditions (rrb = 0.77), with no significant differences between feedback sources."
Discussion¶
Related Claims¶
- Feedback improves learning by a medium amount on average, but its effect varies widely with the information it carries, and a substantial share of feedback effects are negative — the general claim this finding bears on, with the studies behind it
- AI multimodal feedback produces post-test learning gains equivalent to educator feedback after controlling for pre-test performance — related
- Randomization produced baseline-equivalent conditions across the four feedback designs — related
- Students' perceptions of mastery, emotions, and satisfaction were similarly high across all three feedback conditions — related
- No significant group differences in knowledge gain or knowledge transfer despite ChatGPT's task-performance advantage — related
- Domain-specific knowledge gains from the learning session were large but comparable across the three experimental conditions — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
ai-teacher-feedback-equal-project-performance-gains |
aliases |
gpt-o4-mini-equivalent-deepseek-noninferior-teacher-feedback |
title |
Project performance improved significantly from pre- to post-feedback across all three feedback conditions with no significant differences between feedback sources |
description |
Project performance improved significantly from pre- to post-feedback across all three feedback conditions with no significant differences between feedback sources |
status |
review |
evidence_strength |
moderate |
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 — grion-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.