Chemistry Teachers’ Self-Regulated Learning in AI-Supported Teaching: An Exploratory Qualitative Study

Güçlü Z., 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

1. Introduction

Artificial intelligence (AI) is increasingly shaping educational practice by offering tools for lesson planning, content generation, assessment, and feedback. In chemistry education, AI-supported applications are used to explain complex concepts, generate instructional materials, and support differentiated learning. While existing research has begun to explore the implications of AI for student learning, much less attention has been paid to how AI influences teachers’ professional learning processes.

Teachers are not only facilitators of learning but also learners who continuously adapt their instructional practices. Self-regulated learning (SRL) provides a useful framework for understanding how teachers plan, monitor, and evaluate their professional actions. In contexts involving technological innovation, such as AI integration, teachers’ capacity for self-regulation becomes particularly important, as they must navigate new tools, evaluate their pedagogical value, and adjust their practices accordingly.

Despite the relevance of SRL for understanding teacher learning, empirical research examining teachers’ SRL processes remains limited, especially within subject-specific domains such as chemistry education. Moreover, studies that explicitly link AI use to teachers’ self-regulated learning are scarce. Addressing this gap, the present study explores how chemistry teachers who already use AI in their teaching regulate their learning and instructional decision-making processes.

This study contributes to research on self-regulated learning by extending SRL theory to teachers’ professional learning in AI-supported teaching contexts. By focusing on chemistry teachers, the study offers subject-specific insights into AI integration in science education. The findings are expected to inform teacher education and professional development initiatives aimed at supporting reflective and self-regulated teaching practices.

2. Theoretical Framework

2.1 Self-Regulated Learning

Self-regulated learning refers to learners’ active processes of goal setting, strategic action, monitoring, and self-reflection (Zimmerman, 2000, 2002). Zimmerman’s cyclical model conceptualizes SRL across three interrelated phases: forethought, performance, and self-reflection. These phases encompass key processes such as planning, task strategy use, help seeking, and self-evaluation.

While SRL research has traditionally focused on students, teachers also engage in self-regulated learning as they design instruction, respond to classroom challenges, and reflect on their practice (Butler, 2002). Teachers’ SRL has been shown to play a critical role in instructional quality and professional development, particularly in complex teaching environments.

2.2 Teachers’ SRL in Technology-Rich Contexts

Technology integration presents both opportunities and challenges for teachers’ self-regulation. Digital tools may support planning and reflection, yet they also increase cognitive demands and require continuous adaptation (Dignath & Büttner, 2018). AI tools, in particular, introduce new forms of support by generating instructional content and feedback, potentially influencing how teachers plan lessons, seek assistance, and evaluate instructional outcomes.

From an SRL perspective, AI may function as a strategic resource that reshapes teachers’ regulatory processes. However, empirical evidence examining these processes remains limited, especially in science education contexts. This study addresses this gap by examining chemistry teachers’ SRL processes in AI-supported teaching.

3. Purpose and Research Questions

The purpose of this exploratory qualitative study is to investigate how the use of AI in chemistry teaching influences teachers’ self-regulated learning processes.

The study addresses the following research questions:

  1. How do chemistry teachers describe their planning processes when integrating AI into their teaching?
  2. How does AI use shape teachers’ task strategies during instructional design and implementation?
  3. How do teachers engage in help-seeking behaviors while using AI for chemistry teaching?
  4. How do teachers evaluate and reflect on their AI-supported teaching practices?


Methodology, Methods, Research Instruments or Sources Used
This study adopts a qualitative exploratory research design. Such an approach is appropriate for examining underexplored phenomena and capturing participants’ perspectives in depth. The design aligns with the Emerging Researchers’ Conference emphasis on work in progress and theory-informed inquiry.
Participants will be ten high school chemistry teachers working at high schools in Turkey who actively integrate AI tools into their teaching practices. Teachers will be selected through purposive sampling to ensure experience with AI-supported instructional activities. The participants are gıing to fill in a sort online questinnaire on if they use AI to enhance their teaching and if they use it for planning, developing strategies, help-seeking and self assessment. The participants will be selected among the ones who use AI actively for such pruposes. Participation will be voluntary, and informed consent will be obtained prior to data collection.
Data will be collected through semi-structured meetings with each teacher. The meetings will focus on teachers’ experiences with AI use and their self-regulation processes related to planning, task strategies, help seeking, and self-evaluation. All meetings will be audio-recorded and transcribed verbatim.
Conclusions, Expected Outcomes or Findings
Data will be analyzed using thematic analysis (Braun & Clarke, 2006). Initial coding will be guided by Zimmerman’s SRL framework, while allowing for inductive themes to emerge. To enhance trustworthiness, coding decisions will be discussed among researchers, and analytic memos will be maintained throughout the analysis process.
Although data collection is ongoing, several expected patterns can be anticipated. It is expected that AI use will support teachers’ planning by facilitating lesson preparation and content organization. Teachers may report adaptive task strategies, particularly when revising or contextualizing AI-generated materials.
In terms of help seeking, AI is expected to function as an immediate and flexible source of support, potentially altering traditional collegial help-seeking practices. Regarding self-evaluation, teachers may engage in more frequent reflection as they assess the pedagogical effectiveness of AI-supported instruction.
References
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Butler, D. L. (2002). Individualizing instruction in self-regulated learning. Theory Into Practice, 41(2), 81–92. https://doi.org/10.1207/s15430421tip4102_4
Dignath, C., & Büttner, G. (2018). Teachers’ direct and indirect promotion of self-regulated learning in primary and secondary school mathematics classes. Learning and Instruction, 55, 1–14. https://doi.org/10.1016/j.learninstruc.2017.07.003
Kramarski, B., & Kohen, Z. (2017). Promoting preservice teachers’ dual self-regulation roles as learners and as teachers. Teaching and Teacher Education, 63, 83–98. https://doi.org/10.1016/j.tate.2016.12.001
Panadero, E. (2017). A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology, 8, 422. https://doi.org/10.3389/fpsyg.2017.00422
Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press.