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AI-based personalized learning and academic performance in computing and engineering higher education: a systematic review and meta-analysis of controlled studies
- Truvace record
- TRV-2026-2730raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- c6d43b555d6b…check it
Introduction The rapid adoption of Artificial Intelligence (AI) tools for personalized learning in higher education has generated growing interest in their effects on student outcomes, yet comparative evidence remains limited. This systematic review and meta-analysis examined whether AI-based personalized learning is associated with differences in objectively measured academic performance, compared with instruction without the AI component, among higher education students. Methods Following PRISMA 2020, Scopus a…
64Gain side
How the two sides scoreClose call
66Problem side
The gain
A meta-analysis of four controlled studies of 420 computing and engineering students found AI-based personalized learning was associated with higher post-course performance than instruction without AI (pooled Hedges' g 1.01, 95% CI 0.33 to 1.68).
The problem
The same four-study meta-analysis of 420 students rated the evidence very low certainty: with small-sample adjustment the interval (-0.10 to 2.11) included no effect, heterogeneity was I2 = 89%, and two of four studies were at serious risk of bias.
A fixed formula sets both scores from the evidence and its sources. Reader votes don’t move them.
What the scores mean
G is the gain side, out of 100. It goes up when the gain is specific, when it measurably helped people and when the sources behind it are strong.
P is the problem side, out of 100. It goes up when the problem is specific, when it measurably hurt people and when the sources behind it are strong.
What this doesn’t fix
Only four studies with 420 students were available, only one had a verifiable randomized design, and no intervention lasted longer than one semester, so the pooled result cannot establish causation or apply beyond computing and engineering courses.
Sources
1 on file- Frontiers in Educationdoi:10.3389/feduc.2026.1970736
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.
Post-intervention academic performance of higher education computing and engineering students with AI-based personalized learning versus instruction without AI
Hedges' g 1.01 (95% CI 0.33 to 1.68)
420 people · search to April 2026
The review’s own words
The pooled estimate was g = 1.01 (95% CI 0.33–1.68; z = 2.93, p = .003).
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
Cite this record
v1Truvace Impact Record TRV-2026-2730, v1: “AI-based personalized learning and academic performance in computing and engineering higher education: a systematic review and meta-analysis of controlled studies.” Truvace, 2026-11-12. /record/TRV-2026-2730 (accessed on the day you copy it). sha256 c6d43b555d6bc2c4…
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