The exponential use of Artificial Intelligence (AI) has sparked interest within the academic community in understanding its impact on society and education. Self-regulated learning (SRL) is essential for students to adopt learning strategies, such as planning, time management, and reflection. Pedagogical approaches that foster SRL support academic success. AI can facilitate the adoption of SRL strategies, as evidenced by the emergence of technologies that track the learning process. Therefore, it is important to understand the educational intentionality of AI-related applications. A literature review of the last five years was conducted in the Scopus, WoS, and ERIC databases with the objectives of 1) identifying which AI technologies are in use and 2) how they are integrated into pedagogical approaches to support SRL. Of the initial 67 articles, after eliminating duplicates and those without access, articles were selected based on inclusion/exclusion criteria. The chosen articles focused on the use of AI in educational settings and support for SRL, resulting in a final corpus of 14 articles. We identified 9 models based on or integrated with AI, namely: Chatbot, Mobile-Assisted Language Learning (MALL), AI Application Storyboards, Learning Management System (LMS), Hybrid Human-AI Regulation (HHAIR), Explainable Artificial Intelligence (XAI), Learning Analytics (LA), Reflective Thinking Promotion Mechanism-based AI supported English writing (RTP-AIEW), and AI-enabled Visualization Reports. Pedagogical approaches using AI focused on monitoring, data collection, feedback, and personalized support to overcome difficulties/questions. The use of Chatbots is the most highlighted for aiding SRL due to their practicality, accessibility, and versatility across various curricular areas. ChatGPT was the most predominant application in the analysed works. Pedagogical approaches that employ AI prove to be innovative and promote SRL.

Self-regulated learning (SRL) significantly influences students' learning behaviors and academic performance. However, research has focused on "between-person" differences, neglecting "within-person" variability. Traditional SRL assessments rely on self-reports, which fail to capture fine-grained behavioral changes (such as hourly variations). We propose a novel approach using mobile sensing to assess SRL through within-person variability. We use passive sensing data from the phones of 211 university students to explore this relationship. To assess behavioral variability, we focus on five sensing behaviors—physical activity, social interactions, sleep, location, and app usage data—and calculate four within-person variability features: standard deviation, circadian rhythm, regularity index, and flexible regularity index. Our findings reveal significant associations between these variability features and self-reported SRL skills, particularly in dimensions such as environment structuring, time management, and help seeking. This research provides new insights into assessing SRL and offers a theoretical foundation for future personalized interventions in educational settings.