Umar Umar, M Bambang Purwanto

AI and Decision Assistance for Enhancing Self-Directed Learning

Introduction

Ai and decision assistance for enhancing self-directed learning. Discover how AI enhances self-directed learning (SDL) through personalized decision assistance, improving resource selection, learning efficiency, and engagement. Explore AI's transformative role in modern education.

Abstract

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

Review

This study, "AI and Decision Assistance for Enhancing Self-Directed Learning," presents a highly relevant and timely exploration into the potential of Artificial Intelligence to mitigate challenges within self-directed learning (SDL). The abstract clearly outlines the paper's objective to investigate AI's capacity for providing personalized decision assistance, addressing critical issues like cognitive overload and motivational barriers. Through a qualitative approach encompassing case studies, expert interviews, and literature reviews, the research effectively demonstrates how AI-driven tools can significantly improve resource selection, learning efficiency, and engagement. The initial impression is that this paper offers a valuable contribution to the discourse on educational technology and pedagogical innovation. A core strength of this research lies in its focus on the practical benefits observed when integrating AI into SDL. The findings compellingly illustrate that personalized recommendation systems and intelligent tutoring platforms empower learners by offering adaptive feedback and customized learning paths, leading to increased autonomy, reduced cognitive load, and heightened motivation. The reported measurable improvements in learning outcomes underscore the efficacy of the proposed AI interventions. Furthermore, the qualitative methodology appears well-suited to capture the nuanced experiences of participants and the specific mechanisms through which AI enhances the complex, individualistic nature of self-directed learning. Despite its optimistic findings, the study commendably acknowledges critical limitations, such as the risks of over-reliance on AI and the technology's current inability to fully grasp emotional and contextual nuances. The explicit recommendation for a hybrid approach—balancing AI with essential human oversight—is a crucial and responsible insight, grounding the technological advancements within a human-centred educational philosophy. Future work could build upon this by detailing practical frameworks for such hybrid models, investigating the long-term socio-emotional impacts of AI integration, and exploring ethical considerations in greater depth. Overall, this study serves as an important call for thoughtful, balanced integration of AI into educational practices to enhance SDL.

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