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TRUVACE RECORD VERSION
record: TRV-2026-2730
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:16:00.814467Z
status: published
lens: trace
sector: education
headline: AI-based personalized learning and academic performance in computing and engineering higher education: a systematic review and meta-analysis of controlled studies
dek: 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…
gain_title: 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).
problem_title: 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.
trace_subject: AI-based personalized learning and academic performance in computing and engineering higher education
gain_reading: 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).
gain_evidence: A random-effects model (REML) yielded a pooled Hedges' g of 1.01 (95% CI 0.33–1.68) | AI-based personalized learning was associated with higher post-intervention academic performance than instruction without the AI component (pooled g = 1.01)
problem_reading: 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.
problem_evidence: with the Hartung-Knapp small-sample adjustment the interval widened to −0.10–2.11 and included no effect | Two studies were at serious risk of bias, and the certainty of evidence was very low.
quick_read: A systematic review and meta-analysis in Frontiers in Education pooled four controlled studies with 420 students (208 in AI groups, 212 in comparison groups), all in computing and engineering courses in Egypt, China and Morocco. The pooled effect was a Hedges' g of 1.01 (95% CI 0.33 to 1.68) favouring AI-based personalized learning on objectively measured performance.

The headline figure is shaky. With a small-sample adjustment the interval ran from -0.10 to 2.11 and included no effect, and the prediction interval (-0.42 to 2.43) also included zero. Study estimates ranged from g = 0.20 to 1.60, and the authors rated the certainty of evidence very low.

The authors say the pooled figure is likely an upper bound and that causal conclusions are not warranted. Only one study was cluster-randomized, and several tested AI bundled with other teaching changes, so the effect describes an instructional package rather than AI alone.
limitation: 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.
tag: Dual reading
key_points: Searches of Scopus and Web of Science in April 2026 found 84 unique records; four controlled studies met the criteria after full-text review of 11. | The studies came from Egypt, China and Morocco and covered C# programming, software engineering, introductory thermodynamics and a graduate ocean-engineering course. | Three interventions used ChatGPT-based tools and one used a knowledge-graph precision-teaching platform. | Individual effect sizes ran from g = 0.20 (cluster-randomized study) to g = 1.60, with heterogeneity of I2 = 89%. | Leave-one-out estimates ranged from 0.80 to 1.22; excluding the two studies at serious risk of bias gave g = 0.91 with a Wald interval of -0.40 to 2.22. | Two studies, carrying 53% of the weight, were at serious risk of bias; one reported an effect size (d = 0.85) inconsistent with its own data, which imply about 1.6. | The pooled estimate exceeds earlier tutoring-system benchmarks of 0.66 and 0.76, which the authors attribute partly to small samples and researcher-developed tests.
rundown: The review followed PRISMA 2020 and included studies where an AI system adapted content, feedback, pacing or recommendations and where a concurrent or historical comparison group existed. Outcomes had to be objective, such as tests, exams or rubric-scored work. Seven full-text reports were excluded, mainly for having no comparison group or no objective outcome.

The included designs varied widely. One study randomly assigned 27 student groups (13 AI, 14 control) to ChatGPT feedback versus instructor feedback and found a small, imprecise difference (g = 0.20). Another allocated a convenience sample to ChatGPT use after three hours of prompt-writing training. A third compared a 2023-2024 cohort with a 2022-2023 historical cohort.

Sensitivity checks kept the direction positive, with pooled estimates between about 0.8 and 1.2. Under small-sample inference the interval included zero in the primary analysis and in every leave-one-out analysis, and excluded zero only when an alternative composite score or rescaled standard deviation was used.

The authors conclude the evidence does not justify expecting uniform or large gains from adding AI tools to these courses. The largest effects came from studies that embedded AI in a deliberate instructional design, such as structured prompting and regular feedback, and they advise institutions to evaluate such tools locally with objective assessments.
sources:
- peer_reviewed | Frontiers in Education | https://doi.org/10.3389/feduc.2026.1970736 | 2026-10-29
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