SELF-REGULATED LEARNING AND AI: A THEORETICAL PERSPECTIVE ON ACADEMIC READING
Keywords:
self-regulated learning, artificial intelligence, academic reading, metacognition, digital literacyAbstract
This article examines the intersection of self-regulated learning (SRL) theory and artificial intelligence (AI) technologies in the context of academic reading. Drawing on contemporary theoretical frameworks, we explore how AI-powered tools can support and enhance self-regulated reading processes among university students. The study synthesizes existing literature on SRL, academic reading strategies, and AI applications in education to develop a comprehensive theoretical model. Our analysis reveals that AI technologies offer significant potential for supporting metacognitive monitoring, strategy selection, and self-assessment during academic reading tasks. However, the effective integration of AI tools requires careful consideration of pedagogical principles, learner autonomy, and the development of critical digital literacy skills.
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