Transforming University Education with AI: A Systematic Review of Technologies, Applications, and Implications

Dinara Кazimova; Gulmira Tazhigulova; Gulmira Shraimanova; Anatoly Zatyneyko; Adilzada Sharzadin

doi:10.3991/ijep.v15i1.50773

Authors

Dinara Кazimova Karaganda University of the name of academician E.A.Buketov, Karaganda, Kazakhstan https://orcid.org/0000-0001-7169-7931
Gulmira Tazhigulova Karaganda University of the name of academician E.A.Buketov, Karaganda, Kazakhstan https://orcid.org/0000-0001-7026-3947
Gulmira Shraimanova Karaganda University of Kazpotrebsoyuz, Karaganda, Kazakhstan https://orcid.org/0000-0001-6543-5193
Anatoly Zatyneyko Karaganda University of the name of academician E.A.Buketov, Karaganda, Kazakhstan https://orcid.org/0000-0002-6960-4203
Adilzada Sharzadin Karaganda University of the name of academician E.A.Buketov, Karaganda, Kazakhstan https://orcid.org/0000-0001-7898-540X

DOI:

https://doi.org/10.3991/ijep.v15i1.50773

Keywords:

Machine Learning, AI, Natural Language Processing, Intelligent Tutoring Systems,, Adaptive Learning, Educational Outcomes, Systematic Review, PRISMA guideline, Theoretical Framework

Abstract

This systematic literature review explores the integration of artificial intelligence (AI) technologies such as intelligent tutoring systems (ITS), machine learning, natural language processing, and adaptive learning platforms in university education. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we evaluated peer-reviewed articles, case studies, and government reports from 2015 onwards. The results demonstrate that AI technologies not only enhance personalized learning and educational outcomes but also streamline administrative functions, transforming educational practices. However, challenges such as ethical issues, data privacy, and algorithmic bias remain. The review underscores the importance of theoretical frameworks like constructivist learning theory and the TPACK framework for effective AI integration. Recommendations are provided for educators, administrators, and policymakers to ensure responsible AI use in university settings. This paper offers insights into the current capabilities and future prospects of AI in higher education, promoting ongoing research and strategic implementation.