Artificial intelligence-based personalised learning in education: a systematic literature review
Helia Farhood, Magnus Nydén, Amin Beheshti, Samuel Müller
Abstract
Personalised learning models can assist students in meeting their unique requirements and objectives for expanding their knowledge, perspective, abilities, and understanding of the educational system. With the advancement of artificial intelligence, technological integration has the potential to play a critical role in personalising the learning experience. This work attempts a systematic review that examines the influence of personalised learning enabled by artificial intelligence on the educational system. The review synthesises the latest research literature (January 2015–June 2025) and includes a total of 125 studies that achieved our inclusion criteria to demonstrate how technology could successfully modify the learning system. Thoroughly examining the role of artificial intelligence–based personalised learning in education is the main focus of this study. In particular, we categorise applications and review algorithms that support personalised learning, compare their approaches, and identify their impact on teaching, learning, and assessment practices across education sectors (K–12, higher education, and institutionally supported online learning). This work contributes to the field of education by providing a much-needed summary of the current state of research and the various opportunities and challenges in this area.