Artificial Intelligence in Education: Mapping Adaptive Learning and Learning Analytics in K–12 Online, Virtual, and Distance Learning

This scoping review examines how artificial intelligence (AI) has been conceptualized and applied in adaptive learning and learning analytics in K–12 online and distance education between 2020 and 2025. Following Arksey and O’Malley’s framework and reported in accordance with PRISMA-ScR, we analyzed 21 empirical studies to explore thematic patterns, methodological trends, and research gaps. Most studies reported gains for learners in engagement, motivation, and self-regulation. However, reported benefits were unevenly distributed and often favored better-resourced learners, particularly in contexts where teacher mediation and institutional support were modest. AI was explicitly integrated in two-thirds of the studies, yet definitional inconsistencies blurred distinctions between genuine intelligence and automated adaptation. Quantitative designs were predominant, largely focusing on performance outcomes as derived from system logs and test data. While a small but growing number of mixed-methods studies have focused on learner experience and teacher mediation, the field remains constrained by methodological consistency and insufficient clarity regarding AI mechanisms. The findings highlight the importance of clearer conceptual frameworks, research designs that are participatory and context-sensitive, and ethical approaches that center teacher expertise and learner participation. This review argues that the transformative potential of AI for adaptive learning depends less on technological sophistication than on equitable, pedagogically informed integration between human judgment and automated systems.