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TRUVACE RECORD VERSION
record: TRV-2026-2839
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:33:17.571022Z
status: published
lens: g_space
sector: education
headline: Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis
dek: Recent developments in artificial intelligence (AI) have revolutionized the field of education as its presence and benefits enable new methods for teaching and learning. Therefore, many empirical studies have been conducted to evaluate the effectiveness of using AI on students' academic achievement. The purpose of this meta-analysis study was to examine the overall effect of AI on students' academic achievement in relation to a set of moderator variables including educational level, role of AI, intervention dura…
gain_title: A meta-analysis of 29 empirical studies with 2,657 students found a significant positive effect of AI on academic performance, with an effect size of 0.924.
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 29 empirical studies with 2,657 students found a significant positive effect of AI on academic performance, with an effect size of 0.924.
gain_evidence: we found a significant positive effect of AI on students’ academic performance, with an effect size of 0.924
problem_reading: (none)
problem_evidence: (none)
quick_read: A meta-analysis published in Computers and Education: Artificial Intelligence pooled 29 empirical studies covering 2,657 participants at different educational levels. It found a significant positive effect of AI on students' academic performance, with an effect size of 0.924.

The authors followed PRISMA guidelines and drew studies from six databases: Scopus, Web of Science, APA PsycINFO, Education Full Text, ERIC and Social Sciences Full Text. They examined whether the effect varied by educational level, role of AI, intervention duration, sample size, learning strategy, subject area and type of AI.

The authors say the result gives a foundation for future research to identify the specific conditions under which AI most effectively improves student learning.
limitation: 
tag: Evidence-backed gain
key_points: The meta-analysis included 29 empirical studies that met its inclusion criteria. | The combined sample was 2,657 participants across different educational levels. | The overall effect size of AI on academic performance was 0.924. | Studies were drawn from six databases, including Scopus, Web of Science and ERIC. | Moderators tested were educational level, role of AI, intervention duration, sample size, learning strategy, subject area and type of AI.
rundown: The researchers searched six academic databases and selected 29 studies under PRISMA guidelines. The studies were coded, calculated and analysed to estimate the overall effect of AI on achievement.

They also set out to test moderator variables: educational level, the role AI played, how long the intervention lasted, sample size, learning strategy, subject area and type of AI. The overall finding was a significant positive effect of 0.924.

The authors conclude the work validates AI's efficacy and offers a base for identifying the conditions under which AI works best for student learning.
sources:
- peer_reviewed | Computers and Education: Artificial Intelligence | https://doi.org/10.1016/j.caeai.2025.100400 | 2025-03-20
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