Author(s)

JiaXin Yang, Di Yao^*

Affiliation(s)

College of Applied Science and Technology, Beijing Union University, Beijing 10010, China.

Corresponding Author

Di Yao

ABSTRACT

With the rapid development of artificial intelligence (AI), intelligent tools and generative models have increasingly entered higher education and reshaped students' learning processes. AI influences how college students access information, complete academic tasks, and regulate their learning, thereby affecting their self-directed learning. This paper provides a narrative literature review of studies published between 2016 and 2026 in Chinese and international databases. The review examines both positive and negative impacts of AI on college students' self-directed learning. Existing research shows that AI can improve learning efficiency, provide personalized feedback, and support learning strategies. However, it may also lead to over-reliance, academic integrity concerns, and cognitive offloading. The findings further indicate that current studies focus mainly on tool application and learning outcomes, while research on underlying mechanisms and long-term effects remains limited. Future research should explore the pathways through which AI influences self-directed learning and develop appropriate evaluation frameworks in higher education contexts.

KEYWORDS

Artificial intelligence; Self-directed learning; Higher education; College students; Narrative review

CITE THIS PAPER

JiaXin Yang, Di Yao. Artificial intelligence and college students' self-directed learning: a review of opportunities, risks, and future directions. World Journal of Educational Studies. 2026, 4(2): 32-35. DOI: https://doi.org/10.61784/wjes3139.

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