Theoretical Frameworks of Self-Regulated Learning in Intelligent Learning Environments
Keywords:
Self-regulated learning, intelligent learning environments, theoretical framework, metacognition, adaptive learning, artificial intelligenceAbstract
This study aims to systematically identify and analyze the theoretical frameworks of self-regulated learning (SRL) within intelligent learning environments, emphasizing the integration of cognitive, metacognitive, motivational, and technological dimensions. This qualitative systematic review examined 12 peer-reviewed articles focusing on SRL in intelligent learning environments. Articles were purposefully selected from major databases including Scopus, Web of Science, IEEE Xplore, and Google Scholar. Data were analyzed through qualitative content analysis using Nvivo version 14. The coding process involved open coding, subcategory formation, and theme extraction to identify major theoretical models underlying SRL in smart environments. Results revealed three overarching themes: (1) theoretical foundations including cognitive, constructivist, and social-cognitive theories; (2) key SRL components such as goal setting, cognitive monitoring, self-motivation, feedback, and technology interaction; and (3) emerging human–technology frameworks encompassing computational-cognitive models, artificial intelligence-based approaches, and adaptive learning systems. Intelligent technologies were found to enhance metacognitive awareness, motivation, and personalized learning through real-time feedback and data-driven recommendations. Traditional SRL theories require conceptual expansion to align with intelligent and data-driven learning contexts. Integrating cognitive, emotional, and technological elements into hybrid frameworks can foster personalized and adaptive self-regulated learning. These findings provide a conceptual foundation for the development of next-generation intelligent learning environments.
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