Reflective integration of generative artificial intelligence (ChatGPT) in mathematics teacher education: preservice teachers' pedagogical reasoning and lesson design - Cahyaningsih, Ujiati; Hartanto, Ahmad Iqbal (Al-Mufid, 2025)
This study examines the interaction of preservice mathematics teachers (PSTs) with Generative Artificial Intelligence (GAI), specifically ChatGPT, during the lesson design and reflective microteaching processes. The research aims to explore how PSTs evaluate mathematical reasoning, adapt pedagogical ideas, and develop reflective practices when using ChatGPT as a cognitive and pedagogical partner. Employing a qualitative descriptive design, the study involved 14 PSTs enrolled in a Mathematics Learning Strategy course at Universitas Majalengka, Indonesia. Data were collected through lesson plans, ChatGPT interaction transcripts, reflective journals, and post-teaching reports, then analyzed using open, axial, and selective coding following Charmaz (2006) and Saldaña (2016). The analysis revealed three major themes: Critical Statistical Reasoning and AI Evaluation, AI as a Reflective Catalyst for Pedagogical Decision-Making, and Pedagogical and Conceptual Constraints of AI Guidance. Results showed that ChatGPT supported PSTs in reformulating objectives, identifying misconceptions, and improving pedagogical structure, but its reasoning occasionally contained conceptual errors in statistics. PSTs perceived ChatGPT as a reflective stimulus rather than a final pedagogical authority, highlighting the need for guided reflection and AI literacy. The study concludes that GAI can foster critical and adaptive thinking when integrated with mentoring and reflective scaffolds. These findings contribute to the growing body of knowledge on AI-supported teacher education, offering implications for curriculum design and the development of AI literacy in mathematics pedagogy.