<![CDATA[Self-regulated learning (SRL) has emerged as a key competency in modern education. It enables students to take charge of their learning by planning, monitoring, and evaluating their progress. As artificial intelligence (AI) advances rapidly, educational technologies such as chatbots, intelligent tutoring systems, and generative AI tools are being increasingly introduced in classrooms and on online platforms. Although these technologies are often integrated to enhance learning outcomes, their specific role in supporting self-regulated learning (SRL) remains understudied. This paper presents a systematic review of 22 peer-reviewed articles published between 2007 and May 2025. The goal is to map how AI technologies contribute to the different phases of SRL using the framework proposed by Winne and Hadwin (1998) and updated by Winne (2018). The findings indicate that most AI tools provide substantial support during the strategy enactment phase, while fewer focus on planning and metacognitive monitoring. Two phases that are central to SRL’s cyclical nature, task understanding and reflective adaptation, receive comparatively little attention. From a dimensional perspective, cognitive and metacognitive processes are the most frequently addressed, whereas motivational and emotional components are often overlooked. Over 80% of the studies reported a positive impact of AI on SRL behaviors, and approximately 65% noted improvements in academic performance. These results underscore the importance of designing AI systems that are intelligent, responsive, empathetic, and aligned with the realities of student learning. Future research should consider developing emotionally aware, multi-phase AI tools, particularly chatbots, that can accompany learners through the full cycle of SRL in a personalized, ongoing manner.]]>
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