Leveraging Generative AI Agent to Promote Teaching Reflection in a K–12 AI Course: Effects on Teachers’ Reflection Self-Efficacy, Instructional Design, and Reflective Thinking

As generative artificial intelligence (GenAI) rapidly evolves, K-12 education is introducing AI courses whose interdisciplinary complexity exposes teachers' limited readiness. Research suggests that teachers can harness GenAI tools not only to enhance instruction but also to scaffold reflection that drives further teaching improvement. Therefore, this exploratory quasi-experimental study developed a customized GenAI agent system to support teachers' reflection on AI-course teaching and to enhance reflection self-efficacy, instructional design, and reflective thinking. A total of sixty in-service teachers were recruited and divided into two experimental groups, the self-reflection group (SRG) and the peer-reflection group (PRG), both supported by the customized GenAI agent system, and one control group (CG) with conventional technology-based reflection for a four-week AI course reflective practice experiment. The results revealed that the GenAI-agent-supported reflection approach could significantly promote teachers' self-reflection efficacy of two experimental groups compared with CG teachers. However, no significant differences between the SRG and PRG teachers' self-reflections efficacy could be found. Moreover, SRG and PRG teachers also outperformed the CG teachers on mutual-reflection self-efficacy. Furthermore, GenAI approach could significantly boost SRG and PRG teachers' instructional design reflection on methods and behavior, however, no significant differences were observed in instructional objectives or content among the three groups. To further explore the effects of GenAI agent support, Epistemic Network Analysis was applied to examine the coded results of teachers' reflective journals. The findings indicated that SRG and PRG teachers demonstrated broader and higher-order reflective thinking, integrating more dialogic and critical elements, whereas CG networks were predominantly descriptive. Overall, the study confirms that a customized GenAI agent can effectively deepen reflective thinking and practice, offering new insights into fostering teachers' professional development within K-12 AI education.