Enterprise AI Analysis
A Systematic Mapping Review at the Intersection of Artificial Intelligence and Self-Regulated Learning
Published: August 01, 2025 by Seyyed Kazem Banihashem, Melissa Bond, Nina Bergdahl, Hassan Khosravi, Omid Noroozi in the International Journal of Educational Technology in Higher Education
Recently, artificial intelligence (AI) has increasingly been integrated into self-regulated learning (SRL), presenting novel pathways to support SRL. While AI-SRL research has experienced rapid growth, there remains a significant gap in understanding the intersection between AI and SRL, resulting in oversight when identifying critical areas necessitating additional research or practical attention. Building upon a well-established framework, from Chatti and colleagues, this systematic mapping review identified 84 studies through the Web of Science, Scopus, IEEE Xplore, ACM Digital, EBSCOHost, Google Scholar, and Open Alex, to explore the intersection of AI and SRL within the four key aspects—Who (stakeholders), What (theory), How (methods), and Why (objectives). The main results revealed that AI-SRL research predominantly focuses on higher education students, with minimal attention to primary education and educators. AI is primarily implemented as an intervention—through adaptive systems and personalization, prediction and profiling, intelligent tutoring systems, and assessment and evaluation—to support students' SRL and learning processes. The direct impact of AI on SRL was primarily focused on the metacognitive and cognitive aspects of SRL, while the motivational aspect of SRL remains underexplored. While over one-third of the AI-SRL studies did not specify an SRL theory, Zimmerman's model of SRL was the most frequently applied among those that did. The use of AI in supporting SRL has extended beyond just focusing on and supporting SRL itself; it has also aimed to enhance various educational and learning activities as end outcomes such as improving academic performance, motivation and emotions, engagement, and collaborative learning. The results of this study extend our understanding of the effective application of AI in supporting SRL and optimizing educational outcomes. Suggestions for further research and practice are provided.
Executive Impact: AI in Self-Regulated Learning
Our deep-dive reveals how AI is transforming self-regulated learning (SRL) across educational settings, offering unprecedented opportunities for enhanced learning outcomes and operational efficiency. Understand the strategic implications for your organization.
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Deep Analysis & Enterprise Applications
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AI-supported SRL initiatives primarily target students (98% of studies), with a strong focus on higher education (74%). Research involving educators (4%) and primary school students is minimal, indicating a significant gap for foundational skill development.
98%
of AI-SRL studies focus on students
An overwhelming majority of AI-supported SRL initiatives are student-centric. While higher education is the primary focus (74% of studies), there's a notable gap in research for primary school students and educators, limiting AI's foundational impact.
A significant portion (39%) of studies lack explicit theoretical grounding. Among those that do, Zimmerman's SRL model (27%) and Winne & Hadwin's model (11%) are predominant, often integrated for a more nuanced understanding.
| SRL Model | Count | Percentage | Key Characteristics |
|---|---|---|---|
| Unclear/Not Specified | 33 | 39% | Lack of explicit theoretical grounding, hindering comparability. |
| Zimmerman's SRL model (2002) | 23 | 27% | Socio-cognitive perspective, iterative phases (forethought, performance, self-reflection). |
| Winne and Hadwin's model (1998) | 9 | 11% | Information Processing theory, metacognition focus, COPES framework. |
| Co- and Socially Shared Regulation of Learning (SSRL) | 4 | 5% | Group-level regulation, collaborative learning contexts. |
| Pintrich's SRL model (2000) | 3 | 4% | Holistic view, four phases intersecting with cognition, motivation, behavior, and context. |
| Other/Combined Models | 12 | 14% | Includes Butler & Cartier, Efklides, Kramarski & Heaysman, Nicol & Macfarlane-Dick, Román & Poggioli, SMART, and integrative approaches. |
AI is predominantly deployed as an intervention (75% of studies) to support student learning. The primary methods include adaptive systems and personalization, profiling and prediction, intelligent tutoring systems, and assessment and evaluation.
Enterprise Process Flow
Adaptive Systems & Personalization (41%)
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Profiling & Prediction (38%)
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Intelligent Tutoring Systems (33%)
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Assessment & Evaluation (3 studies)
Key AI Applications in SRL
Adaptive systems & personalization are most common, featuring chatbots (N=15), dashboards (N=11), and recommender systems (N=4) to provide tailored feedback. Profiling and prediction utilize AI to identify SRL behaviors and predict academic performance. Intelligent Tutoring Systems (ITS) like MetaTutor (N=11) offer interactive, individualized learning, while Assessment & Evaluation (N=3) uses automated grading for efficient feedback.
AI's direct impact on SRL focuses heavily on metacognition (73 studies) and cognition (51 studies), with less attention on motivation (20 studies). Broader educational outcomes include improved academic performance (48%) and enhanced engagement (12%).
73 Studies
AI Directly Impacts Metacognitive Aspects of SRL
AI interventions are highly effective in enhancing students' metacognitive skills, including planning, monitoring, and evaluation. This leads to improved self-awareness and strategic learning, foundational for complex tasks.
48%
Academic Performance Improvements
Nearly half of the studies (48%) report that AI-supported SRL significantly improves academic performance across subjects like language, math, and writing, demonstrating a tangible return on investment in AI integration.
Broadening Educational Benefits
Beyond direct SRL impacts, AI enhances student motivation (14%), engagement (12%), and emotional states (11%). It also fosters collaborative learning (8%), improves reasoning skills (6%), and creates personalized learning behavior profiles (5%), leading to a more dynamic and responsive educational environment.
Calculate Your Potential AI-SRL ROI
Estimate the potential return on investment for integrating AI-powered self-regulated learning solutions within your organization. Tailor the inputs to your specific context.
Your AI-SRL Implementation Roadmap
A strategic phased approach for integrating AI into your self-regulated learning initiatives, maximizing impact and ensuring sustainable growth.
Phase 1: Assessment & Strategy (1-3 Months)
Conduct a comprehensive audit of existing learning processes and SRL needs. Define clear objectives, identify key stakeholders (students, educators, administrators), and select suitable AI technologies based on theoretical alignment (e.g., Zimmerman's model).
Phase 2: Pilot & Development (3-6 Months)
Develop and customize AI-SRL interventions (e.g., adaptive systems, intelligent tutoring systems) for a pilot group. Focus on data collection methodologies (multimodal, log data) and iterative refinement based on initial performance and feedback.
Phase 3: Integration & Scaling (6-12 Months)
Expand AI-SRL tools to broader populations, ensuring robust ethical guidelines and transparency are met. Implement automated feedback and profiling systems, while continuously monitoring long-term impact on academic performance, motivation, and engagement.
Phase 4: Optimization & Future-Proofing (Ongoing)
Leverage advanced analytics to optimize AI interventions. Explore emerging AI capabilities, foster interdisciplinary collaboration, and adapt to evolving educational needs, ensuring the AI-SRL ecosystem remains effective and relevant.
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