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TRUVACE RECORD VERSION
record: TRV-2026-2774
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:22:04.030169Z
status: published
lens: g_space
sector: education
headline: How does artificial intelligence compare to human feedback? A meta-analysis of performance, feedback perception, and learning dispositions
dek: This exploratory meta-analysis synthesises current research on the effectiveness of Artificial Intelligence (AI)-generated feedback compared to traditional human-provided feedback. Drawing on 41 studies involving a total of 4813 students, the findings reveal no statistically significant differences in learning performance between students who received AI-generated feedback and those who received human-provided feedback. The pooled effect size was small and statistically insignificant (Hedge’s g = 0.25, CI [−0.11…
gain_title: A meta-analysis of 41 studies with 4,813 students found no statistically significant difference in learning performance between AI-generated and human feedback (Hedge's g = 0.25, CI −0.11 to 0.60), suggesting AI feedback may be as effective.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 41 studies with 4,813 students found no statistically significant difference in learning performance between AI-generated and human feedback (Hedge's g = 0.25, CI −0.11 to 0.60), suggesting AI feedback may be as effective.
gain_evidence: no statistically significant differences in learning performance between students who received AI-generated feedback and those who received human-provided feedback | indicating that AI feedback is potentially as effective as human feedback
problem_reading: (none)
problem_evidence: (none)
quick_read: An exploratory meta-analysis pooled 41 studies covering 4,813 students and compared learning performance after AI-generated feedback with performance after human-provided feedback. It found no statistically significant difference, with a small pooled effect (Hedge's g = 0.25, CI −0.11 to 0.60).

A separate analysis limited to language and writing studies reached similar findings, though results varied widely between studies (I2 = 95%). Students' perceptions of the feedback showed a small, negative and statistically insignificant effect (g = −0.20, CI −0.67 to 0.27).

The authors read the results as suggesting AI feedback is potentially as effective as human feedback, and recommend a hybrid approach that combines AI's scalability with the empathetic, contextual qualities of human feedback.
limitation: The analysis is exploratory, the confidence interval for the pooled effect spans zero, and heterogeneity across studies was very high (I2 = 95%), so the result is imprecise.
tag: Evidence-backed gain
key_points: The meta-analysis pooled 41 studies with a combined 4,813 students. | Pooled learning performance effect was small and not significant (Hedge's g = 0.25, CI −0.11 to 0.60). | A language and writing subset gave similar results. | Heterogeneity stayed high (I2 = 95%) across studies. | Perception of feedback leaned slightly negative toward AI but was not significant (g = −0.20, CI −0.67 to 0.27). | The authors advocate a hybrid model of AI and human feedback.
rundown: The analysis, published in Educational Psychology in 2025, is described as exploratory. It compared AI-generated feedback with traditional human-provided feedback across 41 studies and 4,813 students, using learning performance as the main outcome.

The pooled effect was small and statistically insignificant, and its confidence interval spans zero, so the data are compatible with AI feedback being somewhat better or somewhat worse. A language and writing sub-analysis confirmed similar findings.

The authors also looked at how students perceived the feedback and found a small negative but insignificant difference. They argue for hybrid use that keeps human empathy and context alongside AI's scalability.
sources:
- peer_reviewed | Educational Psychology | https://doi.org/10.1080/01443410.2025.2553639 | 2025-09-24
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