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99 ERC SES 03 R: Self-Regulation and Learning Processes
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99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper Artificial Intelligence and Self-Regulated Learning in Education: A Narrative Review of Empirical Research (2020–2025) and Implications for European Educational Futures TED University, Turkey (Türkiye) Presenting Author: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:
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. References Baumeister, R. F., & Leary, M. R. (1997). Writing narrative literature reviews. Review of General Psychology, 1(3), 311–320. European Commission. (December, 2025). European Education Area: Quality education and training for all. https://education.ec.europa.eu/about-eea Fan, Y., Lim, L., Van der Graaf, J., Kilgour, J., Raković, M., Moore, J., Molenaar, I., Bannert, M., & Gašević, D. (2022). Improving the measurement of self-regulated learning using multi-channel data. Metacognition and Learning, 17(3), 1025–1055. https://doi.org/10.1007/s11409-022-09304-z Järvelä, S., Molenaar, I., & Nguyen, A. (2023). Advancing SRL Research with Artificial Intelligence. Computers in Human Behavior, 107, 847. https://doi.org/10.1016/j.chb.2023.107847 Jebur, G., Al-Samarraie, H., & Alzahrani, A. I. (2022). An adaptive Metalearner-based flow: A tool for reducing anxiety and increasing self-regulation. User Modeling and User-Adapted Interaction, 32(3), 469–501 Liao, X., Zhang, X., Wang, Z., & Luo, H. (2024). Design and implementation of an AI -enabled visual report tool as formative assessment to promote learning achievement and self-regulated learning: An experimental study. British Journal of Educational Technology, 55(3), 1253–1276. https://doi.org/10.1111/bjet.1342 Molenaar, I. (2022). The concept of hybrid human-AI regulation: Exemplifying how to support young learners’ self-regulated learning. Computers in Human Behavior Reports, 4, 100130. https://doi.org/10.1016/j.chbr.2022.100130 Molenaar, I., de Mooij, S., Azevedo, R., Bannert, M., Järvelä, S., & Gašević, D. (2023). Measuring self-regulated learning and the role of AI: Five years of research using multimodal multichannel data. Computers in Human Behavior, 139, 107540. https://doi.org/10.1016/j.chb.2022.107540 Moon, J., Yeo, S., Banihashem, S. K., & Noroozi, O. (2024). Using multimodal learning analytics as a formative assessment tool: Exploring collaborative dynamics in mathematics teacher education. Journal of Computer Assisted Learning, 40(6), 2753–2771. https://doi.org/10.1111/jcal.13028 Vuorenmaa, E., Järvelä, S., Dindar, M., & Järvenoja, H. (2023). Sequential patterns in social interaction states for regulation in collaborative learning. Small Group Research, 54(4), 512–550. https://doi.org/10.1177/10464964221137524 Wang, C. Y., & Lin, J. J. (2023). Utilizing artificial intelligence to support analyzing self-regulated learning: A preliminary mixed-methods evaluation from a human-centered perspective. Computers in Human Behavior, 144, Article 107721. https://doi.org/10.1016/j.chb.2023.107721 Wang, W. S., Pedaste, M., Lin, C. J., Lee, H. Y., Huang, Y. M., & Wu, T. T. (2024). Signaling feedback mechanisms to promoting self-regulated learning and motivation in virtual reality transferred to real-world hands-on tasks. Interactive Learning Environments, 1–16. Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14, 1261955. https://doi.org/10.3389/fpsyg.2023.1261955 Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi. org/10.1207/s15430421tip4102_2 99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper Executive Functioning as a Condition for Literacy Engagement: A Classroom-Based Practitioner Inquiry in the Early Primary Years Dublin City University Presenting Author:A growing body of research positions executive functioning (EF); including inhibitory control, working memory, and cognitive flexibility, as closely linked to children’s readiness to engage with classroom learning, in interaction with early academic skills (Blair & Raver, 2015; Diamond, 2013). Within early education, learning-related skills are commonly defined as the capacity to control, direct, and plan actions in academic contexts and are conceptualised as interrelated with social-emotional competence and classroom factors (McClelland et al., 2007). Across this readiness literature, EF and behavioural self-regulation are discussed in relation to attention control (often framed as executive attention), inhibitory control, and working memory, and are positioned within conceptual models linking learning-related skills to academic achievement, including reading and vocabulary. From a literacy-specific perspective, models of writing development emphasise that transcription and self-regulation place substantial demands on limited cognitive resources in younger writers, constraining higher-order composition processes when handwriting and spelling are not yet sufficiently fluent (Graham & Harris, 2000). Research also highlights the connections between writing and reading systems (“language by hand” and “language by eye”), supporting a view of literacy learning as multi-component and interdependent rather than a single skill domain (Berninger et al., 2002). This paper presents a classroom-based practitioner inquiry examining how attention to EF demands shapes pupils’ engagement with literacy learning in an early primary classroom. Rather than evaluating an intervention or measuring pupil outcomes, the inquiry foregrounds teacher learning and instructional reasoning through the following questions:
The inquiry is grounded in developmental and psychobiological models of self-regulation, which conceptualise EF as an integrated system shaped by biological, emotional, and contextual factors (Blair & Diamond, 2008; Blair & Raver, 2015). The analytic focus is the teaching-learning relation: how task design and instructional moves mediate learners’ participation under varying EF demands. Drawing on this literature, the study adopts the stance that executive functioning should be understood not as a learner deficit but as a classroom condition that shapes how literacy tasks are experienced and enacted, particularly where task complexity, transcription demands, and attentional shifts intersect with developing self-regulation. The contribution is a practice-proximal didactic lens for identifying and redesigning EF demand points in early literacy tasks, especially where multiple cognitive demands converge. Methodology, Methods, Research Instruments or Sources Used The study adopts a qualitative practitioner inquiry design, consistent with established traditions of teacher research that conceptualise inquiry as systematic, intentional investigation by teachers into their own classroom practice (Cochran-Smith & Lytle, 1993). Within this framework, the purpose of the inquiry is not to evaluate an intervention or measure pupil outcomes, but to generate professional knowledge through reflective analysis of teaching–learning interactions as they unfold in situ. Data consist solely of the teacher-researcher’s professional artefacts, including structured observation notes, reflective journals, and post-lesson analytic memos generated during literacy instruction across one school term. The use of teacher-generated documents as data aligns with qualitative inquiry approaches that recognise the researcher as the primary analytic instrument and position meaning-making as central to analysis (Creswell, 2013). No recorded pupil data (audio or video), assessment scores, or identifiable pupil records were collected; the dataset comprises only the teacher’s contemporaneous notes and analytic memos. Observations focused on instructional moments where pupils appeared to experience difficulty sustaining attention, managing task demands, or engaging with literacy activities. All notes were written in de-identified form and analysed inductively to identify recurring patterns related to executive functioning demands (e.g., task length, transcription load, attentional shifts, transitions between lesson components). Analytic procedures involved iterative cycles of reflection, coding, and memo writing, informed by sensitising concepts from executive functioning and literacy research. An audit trail was maintained through dated notes, coding iterations, and analytic memos linked to specific lesson episodes to support transparency of interpretation. Reflection was treated as an analytic process rather than unstructured introspection, drawing on typologies of reflective practice that emphasise descriptive, comparative, and interpretive reasoning in professional inquiry (Jay & Johnson, 2002). To reduce confirmation bias, discrepant episodes were actively sought and coded, including lessons where anticipated EF demand points did not materialise. Consistent with qualitative inquiry traditions, the analysis prioritised situated understanding of instructional decision-making rather than claims of effectiveness, generalisability, or causal impact (Creswell, 2013). Conclusions, Expected Outcomes or Findings The inquiry generated three provisional insights, presented explicitly as teacher learning. First, classroom literacy tasks were experienced by the teacher as requiring levels of sustained attention and working memory that necessitated task redesign at points of peak EF demand in order to support participation. This aligns with accounts of executive functioning and self-regulation as central to early learning engagement and readiness, and with conceptualisations of EF in terms of executive attention, inhibitory control, and working memory in early educational contexts (Blair & Raver, 2015; McClelland et al., 2007). Second, heightened teacher awareness of executive functioning demands was associated with more deliberate instructional choices, including reduced simultaneity (fewer concurrent demands), shorter instructional segments, and pacing adjustments. These decisions were oriented toward reducing unnecessary processing demands where transcription competed with composition and meaning-making (Graham & Harris, 2000). Third, the inquiry supported a shift in the teacher’s interpretation of off-task drift, particularly during transitions and multi-step writing tasks, which was increasingly analysed as a potential indicator of misalignment between task demands and developing self-regulation capacities rather than as behavioural non-compliance. These outcomes are reported as professional learning and reflective judgement, not evidence of causal impact. References Baddeley, A. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive Sciences, 4(11), 417–423. https://doi.org/10.1016/S1364-6613(00)01538-2 Berninger, V. W., Abbott, R. D., Abbott, S. P., Graham, S., & Richards, T. (2002). Writing and Reading: Connections Between Language by Hand and Language by Eye. Journal of Learning Disabilities, 35(1), 39–56. https://doi.org/10.1177/002221940203500104 Blair, C., & Diamond, A. (2008). Biological processes in prevention and intervention: The promotion of self-regulation as a means of preventing school failure. Development and Psychopathology, 20(3), 899–911. https://doi.org/10.1017/S0954579408000436 Blair, C., & Raver, C. C. (2015). School Readiness and Self-Regulation: A Developmental Psychobiological Approach. Annual Review of Psychology, 66(1), 711–731. https://doi.org/10.1146/annurev-psych-010814-015221 Diamond, A. (2013). Executive Functions. Annual Review of Psychology, 64(1), 135–168. https://doi.org/10.1146/annurev-psych-113011-143750 Diamond, A., & Ling, D. S. (2016). Conclusions about interventions, programs, and approaches for improving executive functions that appear justified and those that, despite much hype, do not. Developmental Cognitive Neuroscience, 18, 34–48. https://doi.org/10.1016/j.dcn.2015.11.005 Graham, S., & R. Harris, K. (2000). The Role of Self-Regulation and Transcription Skills in Writing and Writing Development. Educational Psychologist, 35(1), 3–12. https://doi.org/10.1207/S15326985EP3501_2 Jay, J. K., & Johnson, K. L. (2002). Capturing complexity: A typology of reflective practice for teacher education. Teaching and Teacher Education, 18(1), 73–85. https://doi.org/10.1016/S0742-051X(01)00051-8 Lungu, M. (2022). The Coding Manual for Qualitative Researchers. American Journal of Qualitative Research, 6(1), 232–237. https://doi.org/10.29333/ajqr/12085 Miles, M. B., Huberman, A. M., & Saldaña, J. (2014). Qualitative data analysis: A methods sourcebook (Edition 3). Sage. 99. Emerging Researchers' Group (for presentation at Emerging Researchers' Conference)
Paper Predictors of Chemistry Achievement: The Interplay Between Motivational Beliefs and Self-Regulatory Strategies Among High School Students Middle East Technical University, Turkey (Türkiye) Presenting Author:Chemistry learning poses persistent challenges in the 21st-century educational landscape due to its abstract nature and the inherent need to integrate symbolic, macroscopic, and microscopic representations. In such cognitively demanding domains, students’ ability to proactively manage their learning processes—rather than relying on passive reception—becomes a critical determinant of academic success. Grounded in the social-cognitive perspective of Self-Regulated Learning (SRL), this study investigates how the interplay between motivational beliefs and self-regulatory strategies influences chemistry achievement within highly competitive, high-stakes testing environments. The theoretical framework is primarily anchored in Pintrich’s (2000) multidimensional model of SRL, which provides a comprehensive structural basis for understanding the cognitive and motivational pillars of learning. Pintrich’s model is unique in its integration of expectancy-value components with cognitive and metacognitive strategies, allowing for a nuanced exploration of why students choose to engage (motivation) and how they execute that engagement (strategies). To complement the structural depth of Pintrich’s framework, this study incorporates Zimmerman’s (2000) cyclical phase model. Zimmerman’s model elucidates the iterative process of learning through three distinct phases: forethought (goal setting and strategic planning), performance (self-control and self-observation), and self-reflection (self-judgment and causal attribution). In the rigorous context of chemistry education, where the complexity of content often leads to cognitive overload, students' success depends not only on the quantity of study time but on the quality of their regulation (Schunk & Greene, 2018). This study focuses on the tension between surface-level processing and deep-level regulation. For instance, while high-stakes assessment environments often push students toward rehearsal strategies (rote memorization), Pintrich’s framework suggests that deeper engagement through elaboration and effective resource management (e.g., time and study environment management) is essential for conceptual mastery. By integrating these two dominant SRL models, the study explores whether autonomous, self-regulated behaviors can serve as a buffer against the detrimental effects of standardized, exam-oriented pressure. From a European and international perspective, this research addresses a vital concern: the "rehearsal paradox" observed in many modern educational systems where high performance in standardized tests does not necessarily equate to scientific literacy or conceptual understanding (Zohar & Dori, 2003). As Europe moves toward fostering "competence-based" science education, understanding the self-regulatory profiles of students in competitive contexts like Turkey offers a valuable comparative lens for international researchers and policymakers aiming to develop resilient, 21st-century learners (Wigfield & Eccles, 2000). Accordingly, this study addresses the following research questions:
Methodology, Methods, Research Instruments or Sources Used This study employed a correlational research design to examine the predictive relationships between self-regulated learning components and chemistry achievement. The sample consisted of 161 twelfth-grade students (science track) from four different districts in Ankara, Turkey. The sample reflected a diverse range of socioeconomic backgrounds, providing a representative cross-section of students facing high-stakes university entrance examinations. Data were collected using two validated and reliable instruments. First, the Turkish adaptation of the Motivated Strategies for Learning Questionnaire (MSLQ), originally developed by Pintrich et al. (1991) and adapted into Turkish by Sungur (2007), was utilized. The MSLQ is a self-report instrument consisting of two main sections: a motivation section and a learning strategies section. In this study, specific subscales corresponding to Pintrich’s SRL framework were selected, utilizing a 7-point Likert scale (1 = not at all true of me, 7 = very true of me). The internal consistency of the subscales was verified, ensuring the instrument's reliability for the local context. Second, chemistry achievement was assessed through a researcher-developed Chemistry Achievement Test (CAT), consisting of 29 multiple-choice items. The CAT was meticulously designed to measure conceptual understanding across the high school chemistry curriculum, moving beyond rote recall to higher-order thinking. The test underwent a rigorous validation process, including content validity checks by an expert panel of chemistry educators and experienced secondary school teachers. Following a pilot study with 100 students, the CAT demonstrated high internal consistency with a Cronbach’s alpha of .853, and item analysis was performed to ensure appropriate difficulty and discrimination indices. Data analysis was conducted using IBM SPSS software. Multiple Linear Regression (MLR) analyses were performed to determine the predictive power of the independent variables (motivational beliefs and learning strategies) on the dependent variable (chemistry achievement). Before the analysis, all necessary statistical assumptions were tested: normality of residuals was checked through histograms and P-P plots; linearity was verified via scatterplots; and the absence of multicollinearity was confirmed using Variance Inflation Factor (VIF < 10) and Tolerance (> .10) values. Two separate regression models were constructed to identify the unique contributions of motivational and strategic components, ensuring a clear differentiation between students' "will" and "skill." Conclusions, Expected Outcomes or Findings Multiple linear regression analyses revealed a differentiated pattern of predictors that highlight the complex nature of SRL in science education. Motivational beliefs significantly predicted achievement, accounting for 15.6% of the variance (R2 = .156, p < .05). Among these, task value emerged as the strongest positive predictor (β = .363, p = .002), indicating that students who perceive chemistry as meaningful and personally relevant are significantly more likely to succeed. This finding reinforces the expectancy-value theory (Wigfield & Eccles, 2000), suggesting that internal motivation is a more powerful driver than external exam pressure. Learning strategies significantly predicted achievement accounting for 20.7% variance (R2 = .207, p < .05). A striking "rehearsal paradox" was identified in this section. While high-stakes environments often encourage rote learning, rehearsal strategies emerged as a significant negative predictor of achievement (β= -.284, p = .018). This empirically demonstrates that reliance on surface-level processing hinders conceptual mastery in chemistry (Zohar & Dori, 2003). Conversely, time and study environment management significantly contributed to success (β= .273, p = .018), highlighting the critical role of resource regulation in competitive contexts. These findings advocate for a transformative pedagogical shift within European science education. To develop resilient, autonomous learners, instruction must move beyond content delivery toward explicit strategy instruction. Specifically, science educators should focus on enhancing students' "task value perceptions" by connecting abstract chemistry concepts to real-world applications. Furthermore, the results emphasize the need to discourage rote memorization in favor of metacognitive and resource management strategies. By offering a comparative lens on how competitive environments shape learning, this study contributes to the international discourse on fostering deep scientific understanding in the 21st century. References Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 451-502). Academic Press. Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1991). A Manual for the Use of the Motivated Strategies for Learning Questionnaire (MSLQ). Schunk, D. H., & Greene, J. A. (Eds.). (2018). Handbook of Self-Regulation of Learning and Performance (2nd ed.). Routledge. Sungur, S. (2007). Modeling the relationships between students' motivational beliefs, self-regulated learning strategies, and effort regulation. Turkish Online Journal of Educational Technology, 6(4), 119-126. Wigfield, A., & Eccles, J. S. (2000). Expectancy–value theory of achievement motivation. Contemporary Educational Psychology, 25(1), 68-81. Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive commentary. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-39). Academic Press. Zohar, A., & Dori, Y. J. (2003). Higher order thinking skills and low-achieving students: Are they mutually exclusive? Journal of the Learning Sciences, 12(2), 145-181. | ||