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Three principal discoveries emerge: (1) Technological progression has evolved through three co\u2010existing paradigms: rule\u2010based architectures, data\u2010driven adaptive systems and generative AI ecosystems, that demonstrate increasingly sophisticated capabilities for human\u2010AI collaboration. (2) While AI\u2010supported SRL interventions yield a moderate overall effect size (\n g<\/jats:italic>\n \u2009=\u20090.507), their impact is uneven; AI is significantly more effective during the task performance phase (\n g<\/jats:italic>\n \u2009=\u20090.574) than in the preparatory forethought phase (\n g<\/jats:italic>\n \u2009=\u20090.401). Notably, generative AI shows markedly superior efficacy across all phases (e.g.\n g<\/jats:italic>\n \u2009=\u20090.709 for forethought,\n g<\/jats:italic>\n \u2009=\u20090.938 for performance), though high heterogeneity suggests these effects are heavily contingent on specific instructional designs. (3) Moderator analysis identifies optimal contexts in secondary education, natural science disciplines, fully online settings and interventions of medium duration (2\u201310\u2009weeks), while also revealing that effects are substantially larger when measured by behavioural traces compared to self\u2010reports. Critically, these findings highlight a persistent performance\u2010competence divide, suggesting that AI's capacity to scaffold immediate task performance may outpace its current ability to cultivate durable, transferable self\u2010regulatory competence. The study discusses the implications of this divide and proposes a research agenda focused on designing AI systems that foster genuine learner autonomy.\n <\/jats:p>\n \n \n \n Practitioner notes<\/jats:title>\n <\/jats:caption>\n \n What is already known about this topic<\/jats:title>\n \n \n \n AI technologies have shown promise in supporting self\u2010regulated learning (SRL) through personalized feedback and metacognitive scaffolding.<\/jats:p>\n <\/jats:list-item>\n \n Previous studies report inconsistent outcomes of AI interventions across SRL phases (forethought, performance, self\u2010reflection), with limited exploration of phase\u2010specific impacts<\/jats:p>\n <\/jats:list-item>\n \n Existing meta\u2010analyses often treat SRL as a unified construct rather than examining how different AI types support distinct self\u2010regulatory processes.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n \n What this paper adds<\/jats:title>\n \n \n \n \n Demonstrates moderate overall effectiveness of AI\u2010supported SRL (\n g<\/jats:italic>\n \u2009=\u20090.507) with differential impacts across phases: strongest during task performance (\n g<\/jats:italic>\n \u2009=\u20090.574), weaker in forethought (\n g<\/jats:italic>\n \u2009=\u20090.401) and self\u2010reflection (\n g<\/jats:italic>\n \u2009=\u20090.464)\n <\/jats:p>\n <\/jats:list-item>\n \n Maps three coexisting AI paradigms\u2014rule\u2010based, data\u2010driven and generative AI\u2014revealing that generative AI achieves superior outcomes across all SRL phases, though effectiveness depends on alignment between AI affordances and specific self\u2010regulatory processes.<\/jats:p>\n <\/jats:list-item>\n \n Identifies optimal implementation contexts through moderator analysis: secondary education settings, natural science disciplines, fully online environments and medium\u2010duration interventions (2\u201310\u2009weeks) yield stronger effects.<\/jats:p>\n <\/jats:list-item>\n \n \n Articulates a critical performance\u2010competence divide, substantiated by quantifying how AI's impact on observable behaviours (\n g<\/jats:italic>\n \u2009=\u20090.751) is more than double its effect on learners' self\u2010reported perceptions (\n g<\/jats:italic>\n \u2009=\u20090.369).\n <\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n \n Implications for practice and\/or policy<\/jats:title>\n \n \n \n Integrate AI paradigms strategically rather than viewing them as replacements: combine rule\u2010based systems' stable scaffolding with generative AI's adaptive dialogue to support the full SRL cycle.<\/jats:p>\n <\/jats:list-item>\n \n Design interventions of medium duration (2\u201310\u2009weeks) to optimize skill acquisition while maintaining engagement, avoiding both novelty effects and scaffold dependency.<\/jats:p>\n <\/jats:list-item>\n \n Adapt AI implementation to disciplinary demands: structured procedural support works well in natural sciences, while social sciences require enhanced support for open\u2010ended inquiry and critical analysis.<\/jats:p>\n <\/jats:list-item>\n \n Develop assessment frameworks that measure delayed, unsupported performance alongside immediate gains to ensure AI fosters genuine self\u2010regulatory competence rather than temporary performance enhancement.<\/jats:p>\n <\/jats:list-item>\n <\/jats:list>\n <\/jats:p>\n <\/jats:sec>\n <\/jats:boxed-text>\n <\/jats:p>","DOI":"10.1111\/bjet.70058","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:04:16Z","timestamp":1773381856000},"page":"1191-1220","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["AI<\/scp>\n support in self\u2010regulated learning: A decade of technological evolution and 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