This study explores the integration of low-cost Internet of Things (IoT) tools in pre-service teacher education and evaluates their impact on teaching practices, self-efficacy, and reflective pedagogy. The research addresses the gap in traditional teacher training, which often lacks real-time feedback and relies heavily on delayed and subjective evaluations. Using a mixed-methods research design, the study involved 85 pre-service teachers divided into an experimental group (n=43) exposed to IoT-enabled classrooms and a control group (n=42) trained through conventional methods. The intervention employed simple yet effective IoT tools such as RFID-based attendance tracking, noise level sensors, and QR-code-based student feedback forms. Quantitative data were collected through IoT metrics and the Self-Efficacy for Teaching with Technology Scale (SETTS), while qualitative insights were gathered via semi-structured interviews, focus group discussions, and mentor field observations. Findings revealed that the experimental group demonstrated significantly higher classroom engagement (27.1% increase), reduced noise levels (11.4% decrease), and improved lesson effectiveness scores (50% improvement), compared to the control group. Post-test SETTS scores also showed statistically significant gains in the experimental group, especially in the domains of reflective practice and technology implementation. Qualitative data further underscored enhanced teacher awareness, data-informed instructional adaptability, and stronger mentor-trainee collaboration in the IoT environment. The study concludes that IoT integration in teacher education not only enriches instructional quality but also fosters real-time pedagogical reflection and adaptability. The research recommends the inclusion of affordable IoT tools in teacher training programs to bridge the theory-practice gap and improve classroom readiness among future educators.
