ARTIFICIAL INTELLIGENCE IN DIGITAL LEARNING AND EDUCATION: A SYSTEMATIC LITERATURE REVIEW

Abstract

Artificial intelligence (AI) is reshaping digital learning environments across higher education, medical training, and K-12 contexts, yet the field lacks a unifying conceptual account linking learner psychology, institutional readiness, and sustainability-oriented outcomes. This systematic literature review (SLR) synthesizes 29 peer-reviewed and conceptual papers spanning AI literacy, AI anxiety, self-regulated learning (SRL), human-AI delegation, algorithmic transparency, and AI-driven institutional transformation. Using a PRISMA-informed selection process, studies were screened for empirical or conceptual relevance to AI in digital learning, yielding a final corpus analyzed for research gaps, objectives, methodological design, data collection approach, and key findings. Results indicate that AI literacy and perceived opportunities consistently predict acceptance and adoption, that anxiety and trust/transparency perceptions act as critical psychological mediators, and that sustainability and organizational-culture factors increasingly shape institutional AI adoption beyond the individual learner level. The review proposes an integrated conceptual framework connecting antecedents (AI literacy, perceived opportunities/challenges, institutional readiness), mediating psychological processes (anxiety, needs satisfaction, trust), and outcomes (acceptance, SRL, work engagement, sustainable innovation). Gaps remain in cross-cultural validation, longitudinal and experimental designs, and integrated governance frameworks, pointing to a research agenda relevant to dissertation work at the intersection of AI, education, and behavioral science.