The trajectory of artificial intelligence for competency-based personalised learning: past, present and future

Purpose: The study aims to reflect on past research, uncover current trends and propose a future research agenda in the field of artificial intelligence (AI) for competency-based personalised learning. Design/methodology/approach: The study followed the SPAR-4-SLR protocol to retrieve 855 articles related to the field indexed in the Scopus database. Performance analysis, network analysis and science mapping were then performed using VOSviewer and the Biblioshiny app. Findings: The analysis identified nine clusters of intellectual structure (healthcare, competencies, learning systems, digital transformation, AI literacy, computer-aided education, AI ethics, e-learning and active learning) and twelve themes (including motor, basic, emerging and niche). Originality/value: Following an extensive review of the literature, this would appear to be the first study to provide a panoramic view of AI for competency-based personalised learning based on the Scopus database. The core gaps in the current literature have been identified and the corresponding future agenda will be instrumental in shaping future research directions in the field.