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 |
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| 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 |
| 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. |
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| ISSN: | 2731-5525 |
| DOI: | 10.1007/s44217-026-01399-9 |
