Conference Agenda
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Please note that all times are shown in the time zone of the conference. The current conference time is: 19th Aug 2026, 21:28:45 EET
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10 SES 03 C: Self-regulated learning and visual data
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10. Teacher Education Research
Long Paper Exploring Teachers’ Perceptions of Self-Regulated Teaching in AI-Integrated Professional Development Oranim Academic College of Education, Israel Presenting Author:Artificial intelligence (AI) is increasingly reshaping educational practice across Europe and internationally, introducing new possibilities for instructional planning, differentiation, feedback, and professional reflection. Recent studies indicate that AI-based tools can support teachers by generating alternative pedagogical strategies, analyzing patterns in student responses, and facilitating reflective decision-making (Hu, 2023; Zhai, 2022). From a pedagogical perspective, such tools may enhance teachers’ capacity for self-regulated teaching (SRT), understood as a cyclical process of goal setting, strategic action, monitoring, and reflective adaptation. However, the literature consistently emphasizes that the educational value of AI is not inherent in the technology itself but depends on how it is framed and embedded within professional learning contexts. European and international research highlights growing concerns that uncritical or instrumental uses of AI may foster cognitive offloading, overreliance on algorithmic recommendations, and a gradual erosion of teachers’ professional autonomy and ethical responsibility (Brandtzaeg et al., 2025). These concerns resonate strongly with European policy discourses that promote human-centred, ethically grounded AI and stress the importance of safeguarding teacher agency in digitally mediated learning environments. Conceptually, this study positions AI not as a pedagogical solution but as a pedagogical catalyst—an external reflective partner capable of provoking professional inquiry, challenging established routines, and supporting metacognitive awareness when integrated into reflective professional development frameworks (Al Darayseh, 2023). Within this framework, teachers’ emotional responses to AI, including curiosity, confidence, uncertainty, or anxiety, are understood as integral components of self-regulatory processes rather than as peripheral reactions, shaping how teachers interpret, adopt, or resist technological innovation. Against this theoretical background, the purpose of the present study is to explore teachers’ perceptions of self-regulated teaching within AI-integrated professional development environments, with a particular focus on teachers’ sense of agency, emotional experiences, and self-reported pedagogical practices at an early stage of AI adoption. Rather than evaluating the effectiveness of a completed intervention, the study adopts an exploratory and interpretive approach aimed at understanding teachers’ subjective orientations toward AI as a pedagogical resource. Specifically, the study addresses three research questions: (1) How do teachers perceive self-regulated teaching in the context of AI-supported professional development, particularly in relation to pedagogical planning, monitoring, and reflective decision-making? (2) What emotional and self-efficacy-related responses do teachers report when engaging with AI as a pedagogical resource, including feelings of confidence, uncertainty, curiosity, or anxiety? and (3) What challenges and opportunities do teachers identify regarding the integration of AI to support self-regulated teaching practices, especially with respect to maintaining professional autonomy and reflective control over instructional processes? By foregrounding teachers’ perceptions, emotions, and regulatory strategies, this study responds to an international research gap, as much of the existing literature prioritizes technological effectiveness over teachers’ lived experiences and professional meaning-making. Situated within European and global debates on digital professionalism, ethical AI, and sustainable educational innovation, the study aims to generate an empirically grounded understanding of how AI can support, rather than constrain, self-regulated teaching. The findings are expected to inform the design of future professional learning models across diverse educational contexts and to provide a conceptual and empirical foundation for subsequent cross-national, longitudinal, and intervention-based research on AI-supported self-regulated teaching. Methodology, Methods, Research Instruments or Sources Used This study employed an exploratory mixed-methods research design combining quantitative and qualitative data to examine teachers’ early-stage perceptions and experiences of self-regulated teaching (SRT) within AI-integrated professional development contexts. A mixed-methods approach was chosen to capture both broad perceptual trends and in-depth insights into teachers’ subjective interpretations, emotional responses, and professional reasoning. The study focuses on mapping initial orientations, tensions, and emerging practices related to AI use in teaching. This approach is particularly appropriate in contexts of rapid technological change, where teachers’ sense of agency and professional judgment are still evolving. The integration of quantitative and qualitative components enabled methodological triangulation, allowing survey patterns to be contextualized through reflective and narrative data. Participants were in-service teachers from diverse subject areas who voluntarily engaged in AI-related professional learning initiatives. The sample included teachers with varying levels of teaching experience, disciplinary backgrounds, and prior familiarity with AI tools, supporting the inclusion of multiple perspectives on SRT and AI integration. Participation was based on informed consent, and teachers were informed about the study’s purpose, voluntary nature, and right to withdraw at any stage. All data were collected anonymously, and identifying information was removed prior to analysis to ensure confidentiality. Quantitative data were collected using a pre–post questionnaire adapted from established self-regulated learning (SRL) instruments and contextualized for teaching practice in AI-supported environments. The questionnaire addressed teachers’ self-reported perceptions and practices related to goal setting, instructional planning, monitoring, reflective evaluation, and pedagogical self-efficacy. Additional items examined cognitive and emotional orientations toward AI, including perceived usefulness, confidence, uncertainty, and concerns regarding professional autonomy. The pre–post design enabled examination of shifts in perceptions over the course of professional learning activities, while acknowledging that the initiative was ongoing. Qualitative data were collected through written reflective tasks and semi-structured interviews with a subset of participants. Reflections invited teachers to articulate their experiences with AI tools, describe pedagogical dilemmas, and reflect on changes in instructional thinking. Interviews provided deeper insight into teachers’ reasoning processes, emotional responses, and perceived challenges and opportunities related to AI-supported SRT. Data analysis combined descriptive statistical analysis of quantitative data with inductive thematic analysis of qualitative data. Integration occurred at the interpretive level, allowing survey trends to be examined alongside qualitative insights and supporting a nuanced understanding of teachers’ early engagement with self-regulated teaching in AI-rich professional development contexts. Conclusions, Expected Outcomes or Findings The findings of this study offer insight into teachers’ early-stage perceptions, emotional responses, and self-reported practices related to self-regulated teaching (SRT) within AI-integrated professional development contexts. Overall, the results depict a dynamic and evolving process marked by growing pedagogical awareness, increased self-efficacy, and persistent tensions regarding the professional role of AI in teaching. Across quantitative and qualitative data, teachers demonstrated a gradual reconceptualization of their professional role in AI-rich environments. AI use prompted reflection on instructional planning, goal setting, and alignment between pedagogical intentions and classroom practices, consistent with contemporary views of teaching as a reflective and adaptive profession. Findings also indicate a rise in pedagogical–technological self-efficacy, particularly in relation to using AI for preparatory and organizational purposes such as planning, content structuring, and task design. At the same time, teachers reported ongoing uncertainty regarding more complex pedagogical applications of AI, especially those involving monitoring student learning, formative feedback, and support for metacognitive reflection. This suggests a gap between the perceived potential of AI and its current implementation in deeper self-regulatory teaching practices. Qualitative data revealed pronounced tensions between efficiency and professional responsibility. While teachers valued AI for saving time and generating ideas, they expressed concerns about dependency, ethical accountability, and loss of pedagogical control. Emotional responses ranged from curiosity and enthusiasm to ambivalence and anxiety, underscoring the emotional and ethical dimensions of AI integration as central to teachers’ self-regulatory engagement. Taken together, the findings suggest that AI currently functions more as a catalyst for professional reflection than as a fully embedded support for self-regulated teaching. Teachers’ engagement with AI remains exploratory and uneven, highlighting the need for sustained professional development frameworks that explicitly address self-regulation, ethical judgment, and reflective AI used to support meaningful and sustainable pedagogical change. References Al Darayseh, A. (2023). Acceptance of artificial intelligence in teaching science: Science teachers’ perspectives. Computers and Education: Artificial Intelligence, 4, Article 100132. https://doi.org/10.1016/j.caeai.2023.100132 Arvatz, A., Hadas, B., Waitzman, R., & Dori, Y. J. (2025). Putting self-regulated learning and teaching into practice: Insights from two science teachers and their students. Instructional Science, 53(5), 973–1003. https://doi.org/10.1007/s11251-024-09663-4 Bellas, F., Guerreiro-Santalla, S., Naya, M., & Duro, R. J. (2023). AI curriculum for European high schools: An embedded intelligence approach. International Journal of Artificial Intelligence in Education, 33(2), 399–426. https://doi.org/10.1007/s40593-022-00315-7 Brandtzaeg, P. B., Følstad, A., & Skjuve, M. (2025). Emerging AI individualism: How young people integrate social AI into everyday life. Communication and Change, 1(1), Article 11. Hu, R. (2023). The transformation of interdisciplinary education in the context of artificial intelligence. International Journal of Education and Humanities, 11(2). Karlen, Y., Hirt, C. N., Jud, J., Rosenthal, A., & Eberli, T. D. (2023). Teachers as learners and agents of self-regulated learning: The importance of different teacher competence aspects for promoting metacognition. Teaching and Teacher Education, 125, 104055. https://doi.org/10.1016/j.tate.2023.104055 Loeng, S. (2020). Self-directed learning: A core concept in adult education. Education Research International, 2020, Article 3816132. https://doi.org/10.1155/2020/3816132 McTighe, J., & Tucker, C. (2022). Developing self-directed learners by design. Educational Leadership, 80(3), 58–65. Michalsky, T. (2024). Metacognitive scaffolding for preservice teachers’ self-regulated design of higher order thinking tasks. Heliyon, 10(2). https://doi.org/10.1016/j.heliyon.2024.e24963 (מומלץ לוודא DOI סופי לפני שליחה) Olatunde-Aiyedun, T. G. (2024). Artificial intelligence (AI) in education: Integration of AI into science education curriculum in Nigerian universities. International Journal of Artificial Intelligence for Digital Marketing, 1(1), 1–14. Zhai, X. (2022). ChatGPT user experience: Implications for education. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4312418 10. Teacher Education Research
Ignite Talk The Effectiveness of Using Visual Data to Develop Students’ Scientific Literacy in Geography Lessons Nazarbayev Intellectual school of Scienceand Mathematics in Karatau district of Shymkent, Kazakhstan Presenting Author:
The contemporary educational landscape is marked by increasingly complex geographic phenomena, including spatial processes, climate change, and human–environment interactions (UNESCO, 2021). To understand these issues in depth, students must develop geography-related scientific literacy, defined as the ability to grasp geographical processes, apply reasoning, and use analytical tools effectively (OECD, 2019). Achieving this requires geography education to equip learners with strong competencies in analysing, interpreting, and reasoning with both quantitative and visual geographic data (NGSS, 2013). Visual data—such as maps, graphs, satellite imagery, and spatial models—plays a particularly important role in helping students decode patterns, observe relationships, and construct evidence-based explanations (Mayer, 2014; Kerski, 2015). Therefore, strengthening scientific literacy has become a central objective of modern geography education (OECD, 2019). Reflecting this trend, fostering scientific literacy constitutes a central educational objective of the Grade 9 Geography curriculum at Nazarbayev Intellectual Schools. In pursuit of this objective, NIS 9th-grade students are expected to interpret data, understand environmental processes, and make evidence-based decisions. To facilitate these skills, visual data such as maps, graphs, satellite images, infographics, and GIS outputs-play a central role in helping learners convert abstract geographical concepts into concrete understanding (Kerski, 2015; van der Schee & Kolvoord, 2010). However, classroom observations and students’ assignments indicate that many students in 9thgrade geography classes at Nazarbayev Intellectual School struggle to analyze these visuals critically. This research investigates how the use of visual data during geography lessons influences the development of students’ scientific literacy, including their ability to interpret information, reason scientifically, and solve real-world problems-following the action research framework outlined by Creswell and Plano Clark (2018). Methodology, Methods, Research Instruments or Sources Used This study employed a mixed-methods approach to collect both quantitative and qualitative data. Quantitative data were acquired from pre- and post-test assessments of scientific literacy, whereas qualitative data were derived from classroom observations, student work analysis, and teacher reflective practices. A total of 32 ninth-grade students participated in this study and were divided into two groups. Students in the experimental group (n = 16) were taught using visual-data-enriched instruction, whereas students in the control group (n = 16) received conventional text-based instruction. The intervention was implemented over 8 weeks and focused on three key topics, during which students in the experimental group interpreted climate change data using line graphs, anomaly maps, and satellite imagery. Using GIS maps and demographic infographics, they analyzed urbanization patterns and studied hazards through case studies that incorporated hazard-risk maps and time-series images. The instruction incorporated scaffolding strategies, including visual decoding protocols, guided questioning, and the Claim Evidence Reasoning (CER) framework, to assist students in developing scientific explanations and reasoning as recommended by McNeill and Krajcik (2012). The quantitative data were extracted using a Scientific Literacy Assessment Rubric adapted from the OECD PISA science framework (OECD, 2019). Students’ performance was evaluated using an assessment rubric across three tasks: map interpretation, graph analysis, and remote-sensing-based inference.Quantitative data were analysed using paired t-tests to assess whether statistically significant differences existed between pre- and post-test scores.Utilising Braun and Clarke’s (2006) thematic coding approach, the qualitative data were analyzed to identify key themes and gain insights into students’ learning processes and engagement within visual-data-enriched lessons. Conclusions, Expected Outcomes or Findings Discussion This outcome aligns with prior research indicating that visual representations facilitate spatial reasoning (Bednarz, 2011) and support evidence-based analytical reasoning (Kastens, 2012). The integration of visual data scaffolds students’ cognitive shift from descriptive observations to more sophisticated analytical thinking, while GIS tools and remote-sensing imagery provide scientific contexts that reflect real-world geographical inquiry. Conclusion The findings provide compelling evidence that integrating visual data into geography education substantially improves key dimensions of students’ scientific literacy, especially in interpreting data, constructing evidence-based reasoning, and articulating scientific explanations. The study recommends the systematic incorporation of visual data analysis across geography curricula, providing focused teacher training in GIS and data visualization, and designing assessment tasks that integrate maps, satellite imagery, and graphical representations. Curriculum designers should be encouraged to embed visual-data skills within educational standards, and schools should provide access to GIS tools and digital imagery to foster inquiry-based learning. In parallel, teacher professional development should focus on enhancing spatial and graphical literacy for effective classroom implementation. This study’s results are limited since it was conducted in a single school, indicating the need for future research with larger and more diverse samples. References Bednarz, S., & Kemp, K. (2011). Geospatial technologies and geographic education. In D. Janelle, B. Warf, & K. Hansen (Eds.), Worldminds: Geographical perspectives on 100 problems (pp. 151–164). Springer. Bednarz, S. W. (2011). Spatial thinking and geographic education: Concepts and approaches. Journal of Geography, 110(1), 4–13. Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). Sage. 10. Teacher Education Research
Ignite Talk Enhancing Students’ Homework Self-Regulation through Digital Links and Structured Pedagogical Guidance Nazarbayev Intellectual school of science and mathematics in Shymkenr, Kazakhstan Presenting Author:Homework organisation, student self-regulated learning, digital support tools in secondary education. In many European and international educational contexts, increasing academic workload and time pressure have made homework a significant source of stress for students. Research shows that poorly structured homework can negatively affect learners’ motivation, autonomy, and academic performance. Despite its pedagogical importance, students often lack effective strategies for organising and completing homework independently. This study addresses the challenge of improving students’ homework performance by integrating digital links to learning resources and clear methodological recommendations into homework tasks. The research responds to current European discussions on learner autonomy, digital competence, and effective use of educational technologies in everyday teaching practice. How does the use of digital links and structured methodological recommendations influence students’ homework completion, time management, and learning motivation? The main objective of the study is to systematise the homework process through the use of digital links and pedagogical guidance aimed at enhancing students’ self-regulation, motivation, and learning efficiency. The study is grounded in:
Methodology, Methods, Research Instruments or Sources Used The study employed a mixed-methods approach combining quantitative and qualitative data.Students from grades 7–12 participated in the research. Methods and Instruments: - Online student survey to analyse attitudes toward homework and identify difficulties; - Pedagogical experiment involving the redesign of homework tasks with embedded digital links and clear recommendations; - Semi-structured interviews to gather students’ qualitative feedback. During the experimental phase, homework assignments were supplemented with: - Direct links to relevant learning materials; - Step-by-step methodological recommendations; - Varied task formats to increase engagement and motivation. Conclusions, Expected Outcomes or Findings The findings demonstrate that: Only 44% of students regularly completed homework before the intervention, while 50% did so irregularly, primarily due to lack of time; After the implementation of digital links and structured guidance, the number of incomplete assignments decreased; Students reported reduced time spent searching for information and better understanding of homework requirements; Learners demonstrated increased independence, responsibility, and motivation. The results suggest a clear causal chain: Digital links and recommendations → clearer task understanding → reduced cognitive load → improved self-regulation → higher motivation → better homework completion and quality. The study contributes to the European dialogue on effective homework practices and digital pedagogy by offering a practical, scalable model for everyday classroom use. The approach aligns with European educational priorities related to: learner autonomy, digital literacy, sustainable workload management. The findings may inform teachers, curriculum designers, and policy discussions on rational and learner-centred homework organisation in secondary education. References Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. European Commission. (2018). DigComp: The Digital Competence Framework for Citizens. Hattie, J. (2009). Visible Learning. Routledge. | ||