Artificial Intelligence and Self-Regulated Learning in Education: A Narrative Review of Empirical Research (2020–2025) and Implications for European Educational Futures

Aydan L. S.

Emerging Researchers' Conference, Tampere, Finlandiya, 17 - 18 Ağustos 2026, ss.1-2, (Tam Metin Bildiri)

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Tampere
  • Basıldığı Ülke: Finlandiya
  • Sayfa Sayıları: ss.1-2
  • TED Üniversitesi Adresli: Evet

Özet

The rapid advancement of artificial intelligence (AI) has significantly reshaped educational practices, particularly in technology-enhanced learning environments. Across European education systems, learner autonomy, engagement, and lifelong learning have been identified as central educational goals, reflected in frameworks such as the European Education Area and the EU Digital Education Action Plan (2021–2027), which emphasize personalized learning, digital competence, and self-directed learning capacities (European Commission, 2025). Within this context, self-regulated learning (SRL) has gained renewed attention as a foundational competence enabling learners to manage their learning processes effectively across formal and informal settings.

SRL refers to learners’ active control over cognitive, metacognitive, motivational, and behavioral aspects of learning (Zimmerman, 2000). While SRL has traditionally been studied as an individual psychological process, recent developments in educational technology, particularly AI-driven systems, have opened new possibilities for supporting SRL dynamically and at scale (Järvelä et al., 2023). AI applications such as adaptive learning systems, intelligent tutoring systems, learning analytics, and predictive modeling can provide personalized feedback, scaffold learning strategies, and support monitoring and reflection processes. Despite growing interest, the rapidly expanding body of research on AI and SRL remains fragmented, with varying theoretical foundations, methodological approaches, and educational targets.

The purpose of this narrative review is to synthesize and critically examine empirical research published between 2020 and 2025 on the relationship between AI and SRL, a period marked by accelerated digitalization and intensified use of AI in education. Drawing on 98 peer-reviewed articles retrieved from Web of Science, Scopus, ERIC/EBSCOhost, and JSTOR, the review identifies dominant research patterns, theoretical frameworks, and gaps, while situating findings within broader European educational priorities.

The review is guided by the following research questions:

  1. How is AI conceptualized and implemented to support self-regulated learning in recent empirical educational research?
  2. Which SRL components namely cognitive, metacognitive, motivational, behavioral are primarily addressed through AI-based interventions?
  3. Which learner populations and educational levels are most frequently targeted?
  4. Which theoretical models of SRL are used in the current AI-SRL research?
  5. What pedagogical and psychological considerations emerge regarding learners’ experiences with AI-supported SRL?

The theoretical framing of the review is informed by Zimmerman’s model of self-regulated learning, which remains the most frequently adopted framework in the literature. At the same time, the review critically reflects on the dominance of this model and considers the need to broaden theoretical perspectives by engaging with alternative SRL frameworks that emphasize motivation, emotion, and social regulation which can be described as perspectives that strongly associate with European socio-cultural and learner-centered educational traditions.

By synthesizing recent empirical evidence, this study seeks to contribute to European educational dialogue on how AI can be used not merely as a technological solution, but as a pedagogically grounded tool to foster autonomous, engaged, and lifelong learners across diverse educational contexts.


Methodology, Methods, Research Instruments or Sources Used
This study adopted a narrative review methodology to provide an integrative and theory-oriented synthesis of recent empirical research on AI and SRL (Baumeister & Leary, 1997). A narrative approach was selected to allow for critical interpretation, identification of conceptual patterns, and exploration of theoretical and pedagogical implications, rather than statistical aggregation.
A systematic search was conducted across four major academic databases widely used in educational research: Web of Science, Scopus, ERIC/EBSCOhost, and JSTOR. The search covered publications from January 2020 to March 2025, reflecting the period of intensified AI adoption in education. Search terms combined variations of artificial intelligence, AI-based learning systems, self-regulated learning, learning regulation, and educational technology.
Inclusion criteria were:
(a) peer-reviewed empirical studies,
(b) focus on educational contexts,
(c) explicit examination of AI applications in relation to SRL or its components, and
(d) publication in English.

Conceptual papers, editorials, and studies unrelated to SRL were excluded.
Following screening procedures, 98 studies were retained for analysis. Each study was coded with respect to: educational level, AI application type, SRL components addressed, theoretical framework, research design, and reported outcomes. Patterns were identified through iterative comparison and thematic grouping.
To enhance transparency and rigor, the review process involved repeated cycles of reading and coding, with particular attention to how SRL was operationalized and how AI was positioned pedagogically. Rather than evaluating effectiveness alone, the analysis emphasized how AI was used to support regulation processes, and which dimensions of SRL were foregrounded or neglected.

Conclusions, Expected Outcomes or Findings
The review reveals several robust patterns in contemporary AI-SRL research. First, AI demonstrates considerable potential to enhance self-regulated learning and indirectly improve student engagement, particularly through personalized feedback, adaptive pathways, and real-time learning analytics (Wang & Lin, 2023;Wei, 2023). These findings align with European policy priorities emphasizing learner autonomy, personalized learning, and lifelong learning competences.
Second, the majority of reviewed studies target higher education students, with AI primarily implemented as an intervention rather than an integrated pedagogical ecosystem (Moon et al, 2024; Vuorenmaa et al. 2023). Common AI applications include adaptive and personalized systems, prediction and profiling tools, intelligent tutoring systems, and assessment and evaluation technologies.
Third, the impact of AI on SRL is predominantly examined in relation to cognitive and metacognitive regulation, such as planning, monitoring, and strategy use. Motivational regulation, however, remains underrepresented, despite its central role in sustaining engagement and persistence which is an issue of particular relevance for inclusive and equitable European education systems.
Fourth, while Zimmerman’s SRL model dominates the theoretical landscape, more than one-third of studies do not specify an explicit SRL framework. This highlights the need for theoretical diversification and stronger conceptual grounding to capture the multifaceted nature of SRL.
Finally, learners generally perceive AI applications as useful for supporting cognitive, metacognitive, and behavioral regulation, but not motivational regulation. Learners emphasize three key considerations for effective AI-supported SRL: learner identity, learner activeness, and learner position within the learning process (Molenaar et al.2023).
These findings invite discussion on how AI-enhanced SRL can be pedagogically designed to address motivational and emotional dimensions and how European educational research can contribute to more holistic, learner-centered AI integration.

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