| Abstract: | This paper examines the role of generative artificial intelligence (Gen-AI) as a contem porary technological paradigm that enables the creation of new content and has the potential to transform pedagogical processes. In teaching and learning, Gen-AI can faci litate personalized, differentiated, and individualized instruction by analyzing learners’ progress and adapting learning strategies to their specific needs. The central learning process that Gen-AI can enhance is self-regulated learning (SRL). The paper discusses the most frequently cited SRL models, which emphasize the interrelated and cyclical nature of the phases of planning, performance, and self-reflection. For each phase, we indicate how Gen-AI can provide meaningful support. Furthermore, we outline how Gen-AI tools can be incorporated into various SRL components: at the cognitive level, by fostering strategies of rehearsal, elaboration, and organization through knowledge synthesis, analogy construction, and the development of conceptual connections; at the metacognitive level, by supporting planning (e.g., setting SMART goals), monitoring un derstanding, and engaging in reflective evaluation; and at the motivational–emotional level, by enhancing self-efficacy, intrinsic motivation, and reducing test anxiety. In additi on, the paper highlights emerging models for integrating Gen-AI, such as HHAIR (Hybrid Human–AI Regulation) and ISAR (Inversion, Substitution, Augmentation, Redefinition), which emphasize the gradual transfer of self-regulatory skills from AI to the learner and the redefinition of learning processes as the highest level of AI-supported learning. De spite the numerous benefits of Gen-AI use, we also draw attention to potential risks, in cluding overreliance on technology, diminished critical thinking, metacognitive passivity, cognitive complacency, and bias. Therefore, the effectiveness of Gen-AI in promoting SRL depends on its purposeful, informed, and reflective use, which can foster deep, re sponsible, and autonomous learning. |
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