Let’s Code with GenAI: Exploring K-12 Teachers’ Self-Efficacy, Value Beliefs, and Coding Performance

Suk Tae Kang, Wanju Huang

While computational thinking (CT) is increasingly vital in K-12 education, teaching it through text-based coding remains challenging for teachers. To address this gap, this study presents and evaluates a self-paced professional development (PD) module, “Let’s Code with GenAI,” created for K-12 teachers to enhance text-based coding and CT. Using a one-group pretest-posttest design, 34 pre-/in-service teachers completed the module in 2025, engaging with instructional videos and hands-on coding activities using Micro:bit and MakeCode (v11.3.22), with a GenAI assistant providing explanations and debugging support. Pre- and post-intervention data were collected using the Teacher Beliefs about Coding and Computational Thinking (TBaCCT) scale and a coding/CT assessment. Posttest scores were higher than pretest scores on teaching efficacy and value beliefs (p < 0.001 for both), as well as in coding self-efficacy (p < 0.001) and CT self-efficacy (p = 0.015). Coding/CT assessment scores were also higher at posttest (p = 0.040). No statistically significant correlations were found between self-efficacy and performance measures. Overall, the findings offer preliminary insights into participants’ post-intervention outcomes in a GenAI-supported, self-paced PD module, including self-efficacy, value beliefs, and coding/CT performance, while underscoring the need for future controlled studies.