How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis

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, str...

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Main Authors: Hui Zhang, Qi Wang
Format: Article
Language:English
Published: SpringerOpen 2025-08-01
Series:International Journal of Educational Technology in Higher Education
Subjects:
Online Access:https://doi.org/10.1186/s41239-025-00544-y
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author Hui Zhang
Qi Wang
author_facet Hui Zhang
Qi Wang
author_sort Hui Zhang
collection DOAJ
description 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.
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spelling doaj-art-6588a97bf01d4cc49d3ed004b23ce6862025-08-20T03:42:56ZengSpringerOpenInternational Journal of Educational Technology in Higher Education2365-94402025-08-0122113710.1186/s41239-025-00544-yHow could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysisHui Zhang0Qi Wang1Artificial Intelligence and Human Languages Lab, Beijing Foreign Studies UniversityArtificial Intelligence and Human Languages Lab, Beijing Foreign Studies UniversityAbstract 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.https://doi.org/10.1186/s41239-025-00544-yGenerative artificial intelligenceKnowledge buildingIn-service teacherEpistemic network analysis
spellingShingle Hui Zhang
Qi Wang
How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
International Journal of Educational Technology in Higher Education
Generative artificial intelligence
Knowledge building
In-service teacher
Epistemic network analysis
title How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
title_full How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
title_fullStr How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
title_full_unstemmed How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
title_short How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
title_sort how could genai work on in service teachers knowledge building process an empirical study based on epistemic network analysis
topic Generative artificial intelligence
Knowledge building
In-service teacher
Epistemic network analysis
url https://doi.org/10.1186/s41239-025-00544-y
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AT qiwang howcouldgenaiworkoninserviceteachersknowledgebuildingprocessanempiricalstudybasedonepistemicnetworkanalysis