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University Study Programs in the AI Era Examined through a PRISMA Guided Meta Analysis of Global Trends and Regional Disparities in the Western Balkans

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Title: University Study Programs in the AI Era Examined through a PRISMA Guided Meta Analysis of Global Trends and Regional Disparities in the Western Balkans
Language: English
Authors: Shemsedin Vehapi, Besime Ziberi
Source: Discover Education. 2026 5.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 29
Publication Date: 2026
Document Type: Journal Articles
Information Analyses
Education Level: Higher Education
Postsecondary Education
Descriptors: Foreign Countries, Artificial Intelligence, Technology Uses in Education, Technology Integration, Higher Education, Meta Analysis, Influence of Technology, Outcomes of Education, Regional Characteristics, Differences
Geographic Terms: Albania, Bosnia and Herzegovina, Kosovo, Montenegro, Macedonia, Serbia
DOI: 10.1007/s44217-026-01399-9
ISSN: 2731-5525
Abstract: 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 Analysis Restricted Maximum Likelihood (REML). The resulting meta-analysis from 20 studies had 16 studies that satisfied overall of the eligibility requirements. The findings indicated a moderate and statistically significant positive impact of integrating AI in higher education, with a pooled effect size of Hedges' g = 0.685 and a 95% confidence interval of [0.389, 0.982]. A significant degree of heterogeneity across studies is also suggested by Cochran's Q = 337.02 (p < 0.001), I² = 95.5%, and τ² = 0.3028. These results demonstrate significant variations in research designs, target populations, and strategies for implementing AI in educational settings, and they lend credence to the application of a random-effects model. The empirical literature's geographical imbalance restricts the findings' generalizability and emphasizes the critical need for regionally diverse research, especially in regions like Europe and the Western Balkans. Because there is an absence of research on AI's use and effects in academic faculties, strategic national research is required to map how it is utilized across fields of study, examine its learning outcomes, and determine its educational consequences.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1513736
Database: ERIC
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: 2731-5525
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  Data: 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 Analysis Restricted Maximum Likelihood (REML). The resulting meta-analysis from 20 studies had 16 studies that satisfied overall of the eligibility requirements. The findings indicated a moderate and statistically significant positive impact of integrating AI in higher education, with a pooled effect size of Hedges&#39; g = 0.685 and a 95% confidence interval of [0.389, 0.982]. A significant degree of heterogeneity across studies is also suggested by Cochran&#39;s Q = 337.02 (p &lt; 0.001), I&#178; = 95.5%, and τ&#178; = 0.3028. These results demonstrate significant variations in research designs, target populations, and strategies for implementing AI in educational settings, and they lend credence to the application of a random-effects model. The empirical literature&#39;s geographical imbalance restricts the findings&#39; generalizability and emphasizes the critical need for regionally diverse research, especially in regions like Europe and the Western Balkans. Because there is an absence of research on AI&#39;s use and effects in academic faculties, strategic national research is required to map how it is utilized across fields of study, examine its learning outcomes, and determine its educational consequences.
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        Value: 10.1007/s44217-026-01399-9
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      – Text: English
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        PageCount: 29
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Technology Integration
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      – SubjectFull: Higher Education
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      – SubjectFull: Meta Analysis
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      – SubjectFull: Regional Characteristics
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      – SubjectFull: Differences
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      – SubjectFull: Albania
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      – TitleFull: University Study Programs in the AI Era Examined through a PRISMA Guided Meta Analysis of Global Trends and Regional Disparities in the Western Balkans
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