Impact of Artificial Intelligence-assisted Learning Intensity on College Students’ Self-directed Learning Ability

AI’s rapid integration into education has boosted learning efficiency but raised concerns about its impact on college students’ self- directed learning (SDL) abilities. This study uses qualitative literature analysis to explore the link between AI-assisted learning intensity and SDL, grounded in Zimmerman’s self-regulated learning theory. It reviews academic sources to build theoretical coherence and ensure methodological rigor. Key findings reveal a “double-edged sword” effect: moderate AI use enhances SDL through personalized goal management, real-time feedback, and motivation reinforcement, improving time management, critical thinking, and self-efficacy. Conversely, overreliance on AI leads to cognitive outsourcing, diminished goal-setting capacity, and superficial reasoning. This study proposes a “boundary-adaptive pairing” model, advocating for adaptive AI intervention strategies tailored to learners’ metacognitive levels and educational stages. Educators should balance technological empowerment with autonomy preservation, ensuring AI serves as a scaffold rather than a replacement. These insights provide a theoretical foundation for optimizing AI integration in higher education to foster sustainable, self-regulated learning outcomes.