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TRUVACE RECORD VERSION
record: TRV-2026-2842
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:33:39.660536Z
status: published
lens: g_space
sector: education
headline: Investigating the effect of artificial intelligence in education (AIEd) on learning achievement: A meta-analysis and research synthesis
dek: Scant information exists about how AI with its different technologies might affect learning achievement in different educational fields across different educational levels and geographical distributions of students. Closing this gap can therefore help stakeholders understand under which learning conditions artificial intelligence in education (AIEd) might work or not, hence achieving better learning achievement. To address this research gap, this study conducted a meta-analysis and research synthesis of the effe…
gain_title: A meta-analysis of 85 quantitative studies with 10,469 students found AI in education had a very large effect on learning achievement (Hedges' g = 1.10), with chatbots showing the largest effects.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 85 quantitative studies with 10,469 students found AI in education had a very large effect on learning achievement (Hedges' g = 1.10), with chatbots showing the largest effects.
gain_evidence: the total effect of AIEd on learning achievement is very large ( g = 1.10, p < 0.001) | 85 quantitative studies ( N = 10,469 participants) | chatbots achieved a very large effect
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis pooled 85 quantitative studies covering 10,469 students and found that AI applications in education had a very large effect on learning achievement, with a Hedges' g of 1.10 (p < 0.001).

The effect varied by technology. Chatbots showed a very large effect, while intelligent tutoring systems and personalized learning systems showed large effects.

The size of the effect also depended on the field of education, level of education, learning mode, intervention duration and where the students were located, so the average figure does not apply equally in every setting.
limitation: The effect of AI in education was moderated by field of education, level of education, learning mode, intervention duration and geographical distribution, so the pooled result varies across settings.
tag: Evidence-backed gain
key_points: The analysis coded 85 quantitative studies with 10,469 participants in total. | Effect sizes were computed as Hedges' g; the overall effect on learning achievement was g = 1.10 (p < 0.001). | Chatbots produced a very large effect on learning achievement. | Intelligent tutoring systems and personalized learning systems produced large effects. | Field of education, level of education, learning mode, intervention duration and geographical distribution each moderated the effect.
rundown: The authors set out to fill a gap: little was known about how different AI technologies affect learning achievement across educational fields, levels and regions. They pooled 85 quantitative studies with 10,469 participants and calculated Hedges' g effect sizes.

The overall effect was g = 1.10, which the authors classify as very large. By technology, chatbots had a very large effect, and intelligent tutoring systems and personalized learning systems had large effects.

The authors also tested five moderating variables: field of education, level of education, learning mode, intervention duration and geographical distribution. All five moderated the effect, which they say shows how and when AI integration in education can be effective.
sources:
- peer_reviewed | Information Development | https://doi.org/10.1177/02666669241304407 | 2025-01-17
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