The Impact of Artificial Intelligence on Personalized Learning: A Systematic Review of Adaptive Learning Systems in Higher Education

Amran Manurung; Ryan Pallacio; Lukman Hakim Sangapan; Prasetyono Hendriarto; Adler Haymans Manurung

doi:10.38035/sijet.v2i4.303

Authors

Amran Manurung Faculty of Economics and Business, Universitas HKBP Nommensen, Medan, Indonesia
Ryan Pallacio Master of Science, Coventry University, United Kingdom
Lukman Hakim Sangapan Management, Institut Bisnis dan Komunikasi Swadaya, Jakarta, Indonesia
Prasetyono Hendriarto Doctor of Management Science, Universitas Bhayangkara Jakarta Raya, Jakarta, Indonesia
Adler Haymans Manurung Doctor of Management Science, Universitas Bhayangkara Jakarta Raya, Jakarta, Indonesia

DOI:

https://doi.org/10.38035/sijet.v2i4.303

Keywords:

Artificial Intelligence, Adaptive Learning, Higher Education, Personalized Learning, Learning Analytics, Educational Technology

Abstract

This study aims to analyze the impact of Artificial Intelligence (AI) on adaptive learning in higher education through a systematic review approach. Data were collected from academic databases—including Scopus, Web of Science, and IEEE Xplore—spanning the period from 2015 to 2025. A total of 62 articles meeting the inclusion criteria were analyzed using thematic analysis. The findings indicate that AI implementation significantly enhances learning effectiveness, student motivation, and academic outcomes through systems capable of tailoring instructional content and strategies to individual learners’ needs. Furthermore, AI supports instructors in early detection of learning difficulties and facilitates data-driven academic interventions. However, the study also identifies key challenges, including infrastructural limitations, low digital literacy among educators, and data privacy concerns, which must be addressed to ensure sustainable and ethical AI integration. In conclusion, the incorporation of AI into adaptive learning holds substantial potential to transform higher education toward a more personalized, inclusive, and evidence-based model.

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