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Effects of generative artificial intelligence on students' learning motivation: evidence from a meta-analysis
- Truvace record
- TRV-2026-2752raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
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Background: The use of generative artificial intelligence (GenAI) in the education setting has expanded quickly. GenAI may enhance learning motivation by providing immediate feedback, adaptive support, and multimodal resources, yet empirical findings remain inconsistent. Aims: To address the controversy of current studies, a meta-analysis was conducted to examine the overall effect of GenAI on students' learning motivation and test whether effects differed across study contexts. Methods: A random-effects meta-an…
The gain
A meta-analysis of 42 controlled studies with 6,059 students found those learning with generative AI scored higher on motivation than comparison groups (Hedges' g = 0.764), though effects varied widely between studies.
What this doesn’t fix
The very high heterogeneity (I2 = 93.7%) means the average effect does not hold across all contexts, and the subgroup differences are exploratory because they reflect study-level characteristics rather than manipulated conditions.
Sources
1 on file- Frontiers in Psychologydoi:10.3389/fpsyg.2026.1832451
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Student learning motivation with generative AI compared with other learning conditions
Hedges' g 0.764 (95% CI 0.566 to 0.962)
42 studies · 6,059 people
The review’s own words
As Hedge's g = 0.764, 95% CI = [0.566, 0.962], 95% PI = [−0.689, 2.217]; z = 7.571, p < 0.001, students using GenAI showed more learning motivation than other students.
Learning motivation with generative AI among secondary school students
Hedges' g 0.360 (95% CI 0.157 to 0.563)
The review’s own words
The effect was stronger among university students ( g = 0.886, 95% CI = [0.637, 1.136]), followed by secondary school students ( g = 0.360, 95% CI = [0.157, 0.563]), and finally primary school students ( g = 0.311, 95% CI = [0.077, 0.544]).
Learning motivation with generative AI in collectivist-culture countries
Hedges' g 0.815 (95% CI 0.585 to 1.046)
The review’s own words
This effect was larger in countries with predominantly collectivist cultures ( g = 0.815, 95% CI = [0.585, 1.046]) than those with individualistic ones ( g = 0.367, 95% CI = [0.147, 0.587]).
Learning motivation with generative AI used on computers
Hedges' g 0.303 (95% CI 0.169 to 0.438)
The review’s own words
Using GenAI on mobile devices ( g = 0.782, 95% CI = [0.545, 1.018]) had a stronger effect on learning motivation than using it on computers ( g = 0.303, 95% CI = [0.169, 0.438]).
Learning motivation with generative AI among university students
Hedges' g 0.886 (95% CI 0.637 to 1.136)
The review’s own words
The effect was stronger among university students ( g = 0.886, 95% CI = [0.637, 1.136]), followed by secondary school students ( g = 0.360, 95% CI = [0.157, 0.563]), and finally primary school students ( g = 0.311, 95% CI = [0.077, 0.544]).
Learning motivation with generative AI used on mobile devices
Hedges' g 0.782 (95% CI 0.545 to 1.018)
The review’s own words
Using GenAI on mobile devices ( g = 0.782, 95% CI = [0.545, 1.018]) had a stronger effect on learning motivation than using it on computers ( g = 0.303, 95% CI = [0.169, 0.438]).
Learning motivation with generative AI among primary school students
Hedges' g 0.311 (95% CI 0.077 to 0.544)
The review’s own words
The effect was stronger among university students ( g = 0.886, 95% CI = [0.637, 1.136]), followed by secondary school students ( g = 0.360, 95% CI = [0.157, 0.563]), and finally primary school students ( g = 0.311, 95% CI = [0.077, 0.544]).
Learning motivation with generative AI in language-related subjects
Hedges' g 0.946 (95% CI 0.630 to 1.262)
The review’s own words
Observed between-subgroup differences across subject domains were found ( Q BET = 23.785, df = 4, p < 0.05), with language-related studies showing the largest pooled effect ( g = 0.946, 95% CI = [0.630, 1.262]), followed by mathematics ( g = 0.711, 95% CI = [0.356, 1.066]) and pedagogy ( g = 0.697, 95% CI = [0.261, 1.133]), then information ( g = 0.291, 95% CI = [0.047, 0.535]), and lowest in science ( g = 0.109, 95% CI = [−0.107, 0.325]).
Learning motivation with generative AI in individualistic-culture countries
Hedges' g 0.367 (95% CI 0.147 to 0.587)
The review’s own words
This effect was larger in countries with predominantly collectivist cultures ( g = 0.815, 95% CI = [0.585, 1.046]) than those with individualistic ones ( g = 0.367, 95% CI = [0.147, 0.587]).
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v1Truvace Impact Record TRV-2026-2752, v1: “Effects of generative artificial intelligence on students' learning motivation: evidence from a meta-analysis.” Truvace, 2026-11-12. /record/TRV-2026-2752 (accessed on the day you copy it). sha256 35d5671bb3e149c2…
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