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TRUVACE RECORD VERSION
record: TRV-2026-2807
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:26:53.827543Z
status: published
lens: g_space
sector: education
headline: University study programs in the AI era examined through a PRISMA guided meta analysis of global trends and regional disparities in the Western Balkans
dek: Artificial intelligence (AI) in higher education is becoming very important research field because of its extensive use, the absence of a thorough framework governing its use, ethical issues, and effects on student learning outcomes. In light of the increasing global commitment to incorporating AI into higher educational institutions, this study synthesizes current empirical findings using the PRISMA 2020 meta-analysis framework and estimate further the impact of AI in higher education using Meta Regression Anal…
gain_title: A meta-analysis of 16 studies found that integrating AI in higher education had a moderate positive effect on learning outcomes (Hedges' g = 0.685, 95% CI 0.389 to 0.982).
problem_title: (none)
trace_subject: (none)
gain_reading: A meta-analysis of 16 studies found that integrating AI in higher education had a moderate positive effect on learning outcomes (Hedges' g = 0.685, 95% CI 0.389 to 0.982).
gain_evidence: pooled effect size of Hedges’ g = 0.685 and a 95% confidence interval of [0.389, 0.982] | moderate and statistically significant positive impact of integrating AI in higher education
problem_reading: (none)
problem_evidence: (none)
quick_read: A PRISMA-guided meta-analysis looked at 20 studies on AI in higher education, of which 16 met the eligibility requirements. Using random-effects meta-regression, it found a moderate, statistically significant positive effect, with a pooled Hedges' g of 0.685 (95% CI 0.389 to 0.982).

The studies differed greatly: Cochran's Q was 337.02 (p < 0.001), I² was 95.5% and τ² was 0.3028. The authors attribute this to variation in research designs, target populations and ways AI was implemented in teaching, so the average effect hides large differences between settings.

The research base is geographically uneven, which limits how far the results can be generalised. The authors call for regionally diverse research, especially in Europe and the Western Balkans, and say national research is needed to map how AI is used across fields of study and what it does to learning outcomes.
limitation: Heterogeneity across the pooled studies was very high (I² = 95.5%), and the geographic imbalance of the literature restricts how far the findings generalise, particularly to Europe and the Western Balkans.
tag: Evidence-backed gain
key_points: The meta-analysis followed the PRISMA 2020 framework and started from 20 studies; 16 met the eligibility requirements. | The pooled effect of AI integration on higher-education outcomes was Hedges' g = 0.685, with a 95% confidence interval of 0.389 to 0.982. | Heterogeneity was very high: Cochran's Q = 337.02 (p < 0.001), I² = 95.5%, τ² = 0.3028. | The authors used meta-regression with restricted maximum likelihood (REML) and a random-effects model. | The published evidence is geographically imbalanced, which restricts generalisability. | The authors say little research exists on AI's use and effects in academic faculties, especially in Europe and the Western Balkans.
rundown: The study was designed as a systematic synthesis of empirical findings on AI in higher education, guided by PRISMA 2020. Of 20 studies identified, 16 satisfied all eligibility requirements and were pooled with meta-regression using restricted maximum likelihood.

The pooled result, Hedges' g of 0.685, indicates a moderate positive effect on learning outcomes. The wide confidence interval (0.389 to 0.982) shows the size of the benefit is uncertain, and an I² of 95.5% means nearly all variation reflects real differences between studies rather than chance.

The authors say this variation reflects differences in research designs, target populations and AI implementation strategies. They argue that the geographic imbalance of the literature restricts generalisability and call for strategic national research in the Western Balkans to map AI use across fields of study and assess its educational consequences.
sources:
- peer_reviewed | Discover Education | https://doi.org/10.1007/s44217-026-01399-9 | 2026-03-27
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