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.

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.

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.

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