A Comprehensive Systematic Review of AI-Driven Approaches to Self-Directed Learning
Muhammad Younas, Dina Abdel Salam El‐Dakhs, Yicun Jiang
Abstract
Artificial intelligence (AI)-enhanced technologies are increasingly being researched and applied in learning and instruction. Previous studies have demonstrated the growing influence of AI tools in improving learning outcomes and transforming the educational landscape. However, limited research has explored learners’ engagement with AI resources to support self-directed learning, and no comprehensive review exists in this context. This study conducts a systematic review to examine the potential directions underlying the use of AI-enabled resources to assist learners in self-directed learning. The review targeted articles from high-profile databases such as Web of Science and ScienceDirect. A range of AI resources, including intelligent tutoring systems and conversational agents, were identified in the reviewed articles for their roles in providing personalized learning support, question-answering assistance, timely feedback, action recommendations, and interactive learning experiences that foster self-directed learning aspirations. The review also demonstrated that AI-supported self-directed learning can have a positive effect on the satisfaction of learners’ autonomy and competence needs. Besides that, prior knowledge of the learners acts as a facilitator for the continued use of AI resources, thereby positively affecting the outcomes of self-directed learning. Although AI in education is still a rare research field, future studies should focus, for example, on the use of concrete AI tools, such as intelligent tutoring systems or dialog systems supporting other forms of self-directed learning.