Generative AI and Self-Directed Learning: A Systematic Review of Emerging Trends and Implications

• In Event: AERA Poster Session Friday 7:45 am
  In Poster Session: ATL Poster Session 2: AI Integration and Outcomes in Postsecondary Settings

Abstract

This systematic review examines twelve empirical studies at the intersection of generative AI (GenAI) and self-directed learning (SDL), focusing on affordances, challenges, implications, and future directions of GenAI supporting SDL. Nine key affordances were identified, including enhanced motivation, personalization, cognitive and emotional support, autonomy, and feedback. Challenges include GenAI's inaccuracy, over-reliance risks, ethical concerns, usability issues, and low AI literacy. The review also critically analyzes methodological limitations and the narrow scope of studied contexts and tools. Future research should refine methods, broaden contexts, integrate tools, address ethical concerns, and build learner competencies. Practical implications emphasize structured scaffolding, strategic tool use, and supporting metacognitive and emotional growth to ensure effective and ethical GenAI-supported SDL.

Authors

• Xiaoying Zheng, Indiana University

• Zixi Li, Indiana University

• Chaoran Wang, Colby College