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TRUVACE RECORD VERSION
record: TRV-2026-2836
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:32:59.704225Z
status: published
lens: g_space
sector: education
headline: Effects of Generative Artificial Intelligence on K-12 and Higher Education Students’ Learning Outcomes: A Meta-Analysis
dek: Generative artificial intelligence (GenAI) has significant potential for educational innovation, although its impact on students’ learning outcomes remains controversial. This study aimed to examine the impact of GenAI on the learning outcomes of K-12 and higher education students, and explore the moderating factors influencing this impact. A meta-analysis of 49 articles showed that the mean effect sizes of GenAI on students’ learning achievement and learning motivation were 0.857 and 0.803, respectively, indica…
gain_title: A meta-analysis of 49 articles found generative AI use was linked to higher student learning achievement (mean effect size 0.857) and learning motivation (0.803) across K-12 and higher education.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 49 articles found generative AI use was linked to higher student learning achievement (mean effect size 0.857) and learning motivation (0.803) across K-12 and higher education.
gain_evidence: the mean effect sizes of GenAI on students’ learning achievement and learning motivation were 0.857 and 0.803, respectively
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis pooling 49 articles examined how generative AI affects the learning outcomes of K-12 and higher education students. The mean effect size was 0.857 for learning achievement and 0.803 for learning motivation, both pointing to a positive impact.

Effects differed by education level, subject, AI interface, how the AI was developed, how students interacted with it and how long the experiment ran. Higher education students saw a greater impact on academic performance, and text interaction worked better than mixed media such as images or audio.

The motivation effect appears to be partly a novelty effect: the authors report that its size decreases over time. They conclude the findings support beneficial effects of GenAI on education and can guide how it is used in teaching.
limitation: The effect on learning motivation is partly a novelty effect, and its size decreases as experiments run longer, so early gains may overstate lasting benefit.
tag: Evidence-backed gain
key_points: The meta-analysis pooled 49 articles covering K-12 and higher education students. | Mean effect size for learning achievement was 0.857. | Mean effect size for learning motivation was 0.803. | Higher education students showed a greater impact on academic performance. | Students interacted more effectively with GenAI through text than through mixed media such as images or audio. | The motivation effect shrinks as experiment time grows, which the authors link to novelty.
rundown: The study set out to settle a controversy over whether generative AI improves learning. It pooled 49 articles and tested which factors change the effect, including education level, subject classification, GenAI interface, GenAI development, interaction approach and experimentation time.

Results showed positive average effects on both achievement (0.857) and motivation (0.803). Impact on academic performance was larger for higher education students than for younger students, and text-based interaction outperformed mixed-media interaction.

The motivation gain was not stable: GenAI has a novel effect on motivation, and the effect size decreases over time. The authors say the results offer insights for optimizing GenAI use in teaching practice.
sources:
- peer_reviewed | Journal of Educational Computing Research | https://doi.org/10.1177/07356331251329185 | 2025-04-11
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