Self‐Regulated Learning Meets AI: Reinterpreting Self‐Regulation, Co‐Regulation, and Socially Shared Regulation in Human–AI Interaction

ABSTRACT Advancing artificial intelligence (AI) has transformed learning and work, yet higher education and professional development programs have not systematically equipped learners for AI‐prevalent environments. This lack of preparation creates uncertainty regarding control, responsibility, trust, and accountability. This article provides a conceptual synthesis and theoretical reinterpretation of self‐regulation, co‐regulation, and socially shared regulation of learning within human–AI interaction. From these constructs, three learner roles emerge: director, scaffolder, and partner. These roles represent contextually appropriate modes of engagement rather than a developmental hierarchy. Central to this framework is prompt engineering, positioned as a regulatory practice that enables learners to maintain strategic control and professional accountability. The proposed framework assists educators and learners in navigating uncertain human–AI settings. Finally, the article discusses practical implications for curriculum design, instructional strategies, and assessment, alongside pedagogical caveats for implementation in higher education and adult learning settings.