Harnessing AI to Support Self-Regulated Learning in Educational and Workplace Settings

About this Research Topic

Background

Self-regulated learning (SRL) encompasses the internal processes—cognitive, metacognitive, emotional, and motivational—that guide individuals in setting personal goals, employing strategies to achieve them, monitoring performance, and adapting behavior accordingly (Zimmerman, 2000, 2011; Pintrich, 2004; Andrade, 2019). Effective self-regulated learners are able to manage their time, self-evaluate, and seek help when needed (Pintrich et al., 1994).

SRL is a key factor in promoting academic and professional success. In 21st-century learning environments—both in education and the workplace—learners are increasingly expected to operate autonomously. Whether it is a student navigating a digital classroom or an employee reskilling in response to industry demands, the ability to self-regulate is essential. Educational research shows that explicitly teaching SRL strategies can significantly improve outcomes, yet these skills are often underemphasized in both formal education and professional training.

Despite its importance, training in SRL remains limited in K–12 and workplace settings, where the focus tends to center on content mastery rather than learning how to learn. As students enter higher education or employees face the need to upskill or reskill, this lack of SRL training becomes a major hurdle. Existing interventions are often time- and resource-intensive, limiting their scalability and feasibility.

Artificial intelligence offers new pathways for embedding SRL support into educational and training contexts in scalable, personalized, and resource-efficient ways. For example, AI-powered tools—such as chatbots, virtual agents, adaptive learning environments, or learning analytics dashboards—can prompt learners to reflect, set goals, and monitor their progress. AI can also support metacognitive feedback, automate formative assessment, and even model SRL behavior. Hybrid Human-AI Regulation (HHAIR) systems blend human guidance with AI-driven feedback to develop SRL competencies. Affective computing enables emotion-aware AI to detect learners’ emotional states via multimodal data to personalize SRL interventions in real time. And large language models (LLMs) and generative AI tools allow for dynamic, context-aware scaffolding of metacognitive strategies, such as prompting reflection, encouraging self-talk, and simulating SRL dialogues.

While research on SRL and AI has grown independently, there is a need for greater integration of these domains. This Research Topic seeks to explore theoretical, empirical, and design-based studies on how AI can be used to develop, support, and assess SRL across educational and workplace contexts.

This Research Topic invites original, theoretical, and empirical contributions that explore the intersection of AI and self-regulated learning across educational and workplace settings. We welcome papers that examine how AI can support SRL in terms of learner engagement, agency, motivation, and skill development.

Relevant topics include (but are not limited to):

-How can AI systems be designed to scaffold SRL in digital learning environments?

-In what ways can AI monitor and assess SRL behaviors effectively and ethically?

-What role do learners’ perceptions and trust in AI play in supporting self-regulation?

-How can generative AI and affective computing be leveraged to provide real-time, personalized SRL support?

-What are effective models of Hybrid Human-AI Regulation (HHAIR) for supporting SRL in inclusive ways?

-How can SRL be embedded into AI-driven upskilling and workplace training systems?

We invite a range of manuscript types, including Original Research, Conceptual Analyses, Systematic Reviews, Meta-Analyses, Case Reports, and Design-Based Studies. Submissions are encouraged from diverse disciplines, including AI, Learning Sciences, Education Policy, Learning Analytics, Assessment, Online Learning, and Professional Development.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Data Report
• Editorial
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Technology and Code

Keywords: Self-regulated learning, AI, K-12 education, higher education, workplace training, upskilling

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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