Approaches to Self-Regulated Learning in the Era of Generative Artificial Intelligence
Keywords:
Self-regulated learning, generative artificial intelligence, digital reflection, intelligent learning environments, personalized learning, technology ethicsAbstract
This study aims to examine and analyze contemporary approaches to self-regulated learning in educational environments equipped with generative artificial intelligence, identifying its conceptual, technological, and ethical dimensions. This research is a qualitative systematic review. Data were extracted from 12 selected scholarly articles from international databases. Analysis was conducted using NVivo 14 software, employing open coding and categorization of subthemes and main themes to identify recurring patterns and achieve theoretical saturation. The analysis revealed that generative AI has redefined the concept of self-regulated learning, transformed the roles of learners and teachers, and enabled cognitive human–machine interaction. Technological tools, including intelligent generative systems, adaptive learning environments, AI tutors, and digital reflection tools, play a key role in enhancing self-regulation and personalizing learning pathways. Ethical challenges, such as data privacy, algorithmic bias, and overreliance on technology, were also identified, highlighting the need for appropriate ethical and educational frameworks. Self-regulated learning in the era of generative AI has evolved into a combination of human self-leadership and technological support, enhancing learners’ capacity to manage their learning processes. Effective implementation requires adherence to ethical principles, strengthening digital literacy, and training teachers as facilitators to maximize positive outcomes and minimize potential negative effects.
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