How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
Hui Zhang, Qi Wang
Abstract
Abstract In-service teacher professional development (TPD) is essential for improving teacher quality and student outcomes. Effective professional development equips teachers to actively engage in problem-solving and meaning construction. However, current online TPD often lacks tailored support, structured analysis, communication, and feedback, limiting teachers’ ability to engage in deep knowledge-building. Generative Artificial Intelligence (GenAI), exemplified by models like ChatGPT, has attracted significant attention for its potential in education, particularly in offering personalized feedback and fostering deep cognitive engagement. This study examines a large language model developed in China to investigate its impact on in-service teachers’ knowledge-building processes. Through analysis of frequency and epistemic network, this study demonstrates that GenAI significantly enhances in-service teachers’ information analysis and critical thinking. It also promotes greater attention to information processing, evaluation, and knowledge transfer during the knowledge-building process, although it performs less effectively in fostering social interaction and collaboration. The study further reveals that GenAI’s impact on knowledge building varies across learning tasks, with its support being particularly significant in higher-order, complex tasks. Building on these findings, the study offers recommendations for professional development for teachers.