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TRUVACE RECORD VERSION
record: TRV-2026-2802
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:25:50.900616Z
status: published
lens: g_space
sector: education
headline: Meta-Analysis of the Impact of Artificial Intelligence-Based Teaching Feedback on Learning Outcomes
dek: This study is grounded in the practice of educational digital transformation, and was designed in strict accordance with the methodological norms of meta-analysis. By systematically sorting out the inconsistent research conclusions on artificial intelligence teaching feedback, we systematically searched and screened the China National Knowledge Infrastructure (CNKI) and Wanfang Database, and formulated strict literature inclusion and exclusion criteria, we ultimately selected 32 eligible empirical studies publis…
gain_title: A meta-analysis of 32 Chinese studies covering 12,486 students found AI teaching feedback raised learning outcomes over traditional feedback, with a pooled Hedges' g of 0.51, a moderate effect.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 32 Chinese studies covering 12,486 students found AI teaching feedback raised learning outcomes over traditional feedback, with a pooled Hedges' g of 0.51, a moderate effect.
gain_evidence: pooled effect size of Hedges' g=0.51 (95% CI [0.42, 0.60], Z=11.24, p<0.001) on learning outcomes
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis pooled 32 empirical studies published in China between 2016 and 2024, covering 12,486 students. It compared AI teaching feedback with traditional feedback and found a pooled effect of Hedges' g=0.51 (95% CI 0.42 to 0.60) on learning outcomes, which the author calls a moderate positive effect.

The size of the gain varied with school stage, feedback type and subject. Primary school pupils benefited most (g=0.62), followed by secondary students (g=0.49) and university students (g=0.38). Immediate and personalized feedback outperformed delayed and standardized feedback.

Heterogeneity between studies was high (I²=83.45%), and the included interventions mostly lasted 1 to 6 months, so lasting effects remain unmeasured.
limitation: The included studies mostly ran 1 to 6 months, so the review could not examine long-term effects of AI teaching feedback; it also drew only on studies published in China.
tag: Evidence-backed gain
key_points: The analysis included 32 independent studies from 19 Chinese provinces and cities, drawn from the CNKI and Wanfang databases, with 12,486 participants in total. | Pooled effect on learning outcomes was Hedges' g=0.51 (95% CI 0.42 to 0.60) under a random-effects model. | By stage: primary school g=0.62 (11 studies), secondary g=0.49 (10 studies), university g=0.38 (11 studies). | Immediate feedback (19 studies) gave g=0.58 against g=0.32 for delayed feedback (13 studies). | Personalized feedback (17 studies) gave g=0.64 against g=0.35 for standardized feedback (15 studies). | By subject: language g=0.57 (12 studies), mathematics and science g=0.48 (11 studies), skill-based subjects g=0.45 (9 studies). | Publication-bias checks found no significant bias: Egger's test p=0.217 and a fail-safe N of 3,824.
rundown: The author followed PRISMA reporting standards and searched two Chinese databases, CNKI and Wanfang. Included studies needed an AI-feedback group and a traditional-feedback control group, plus data for calculating effect sizes. Two researchers screened studies and extracted data independently, and the analysis used CMA 3.0 software.

Between-study heterogeneity was high (Q=187.23, df=31, I²=83.45%), so a random-effects model was used. The funnel plot was roughly symmetrical, Egger's test gave p=0.217, and the fail-safe N of 3,824 far exceeded the 170 threshold.

Group differences were statistically significant for stage (Q=8.72, p<0.05) and subject (Q=6.34, p<0.05). The author says language learning benefits from frequent instant feedback, while mathematics and skills subjects need deeper feedback that current AI tools struggle to give.

The author recommends educators prioritize real-time, personalized AI feedback matched to students' stage and subject, and calls for further work on adaptive AI feedback technologies.
sources:
- peer_reviewed | International Journal of Education and Humanities | https://doi.org/10.54097/zq9yk850 | 2026-04-20
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