AI and Decision Assistance for Enhancing Self-Directed Learning

Abstract. Self-directed learning (SDL) is critical in modern education, empowering learners to independently manage their learning processes and fostering lifelong learning. Despite its advantages, SDL presents significant challenges, including cognitive overload, motivational barriers, and ineffective decision-making. This study explores the potential of Artificial Intelligence (AI) to enhance SDL by providing personalized decision assistance. Using a qualitative approach—through case studies, expert interviews, and literature reviews—the study examines the use of AI-driven tools such as adaptive learning platforms, personalized recommendation systems, and intelligent tutoring systems. Findings reveal that these technologies improve resource selection, learning efficiency, and engagement by offering real-time, adaptive feedback and personalized learning paths. Participants reported increased autonomy, reduced cognitive load, and increased learners’ motivation, leading to measurable improvements in learning outcomes. However, limitations such as over-reliance on AI and the lack of emotional and contextual understanding underscore the need for a hybrid approach that combines AI with human oversight. This study highlights the potential of AI to significantly reshape and enhance self-directed learning (SDL) by making it more personalized, efficient, and accessible. It also offers recommendations for integrating AI into educational practices in ways that balance technological innovation with essential human-centred guidance