Student Agency and Self-Regulated Learning with Generative AI: When, How, and for Whom Does AI Support Learning?

Generative artificial intelligence is rapidly becoming part of learners’ everyday academic work, yet its psychological consequences are not uniform. AI tools can provide explanations, feedback, prompts, and adaptive support, but they may also encourage passive reliance, reduce productive struggle, or obscure how students plan and evaluate their own learning. Educational psychology offers the constructs needed to explain these contrasting outcomes, particularly self-regulated learning, student agency, motivation, metacognition, cognitive engagement, and beliefs about learning. Examining generative AI through these processes moves the debate beyond access and adoption toward a more precise account of when AI strengthens learning, when it weakens learner control, and why effects differ across students, tasks, disciplines, and educational contexts.This Research Topic aims to establish a psychologically grounded account of how generative AI shapes learning processes and outcomes. It seeks studies that identify the conditions under which AI supports or undermines goal setting, strategic action, monitoring, reflection, persistence, autonomy, and calibrated help seeking. Particular attention should be paid to learner differences, task demands, instructional design, teacher mediation, and sociocultural context. Contributions should connect AI use to explicit psychological theory and examine mechanisms, not merely perceptions of usefulness or frequency of adoption. We especially encourage longitudinal, experimental, mixed-methods, and design-based research; studies using validated psychological measures; and qualitative work that captures agency and metacognitive decision making. Together, the collection will clarify when, how, and for whom generative AI can support learning while identifying risks of overreliance and weakened self-regulation.Areas of interest include, but are not limited to:Generative AI across phases of self-regulated learningStudent agency, autonomy, and human-AI co-regulationMotivation, self-efficacy, beliefs about learning, and persistenceMetacognitive monitoring, calibration, and reflectionHelp seeking, feedback use, and strategic decision makingCognitive offloading, epistemic beliefs, overreliance, and productive struggleLearner differences, equity, culture, and educational contextTeacher mediation and psychologically informed AI-supported designSubmissions must foreground psychological constructs and explain how they are measured or examined. Studies focused primarily on technology acceptance (e.g., TAM models), tool usability, or general EdTech implementation without an explicit investigation of human cognition, motivation, or self-regulatory mechanisms fall strictly outside the scope of this collection.This Research Topic welcomes Original Research, Systematic Reviews, Meta-analyses, Reviews, Mini Reviews, Methods, Theory and Hypothesis, Perspectives, and Brief Research Reports. Topic editors: Eva María Aguaded Ramírez, Matt Smith Source: https://www.frontiersin.org/research-topics/85471/student-agency-and-self-regulated-learning-with-generative-ai-when-how-and-for-whom-does-ai-support-learning