How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis
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| I publikationen: | International Journal of Educational Technology in Higher Education vol. 22, no. 1 (Dec 2025), p. 47 |
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Springer Nature B.V.
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| Länkar: | Citation/Abstract Full Text Full Text - PDF |
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MARC
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| 100 | 1 | |a Zhang, Hui |u Beijing Foreign Studies University, Artificial Intelligence and Human Languages Lab, Beijing, China (GRID:grid.443245.0) (ISNI:0000 0001 1457 2745) | |
| 245 | 1 | |a How could GenAI work on in-service teachers’ knowledge building process? An empirical study based on epistemic network analysis | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Teaching | ||
| 653 | |a Pedagogy | ||
| 653 | |a Data processing | ||
| 653 | |a Feedback | ||
| 653 | |a Professional development | ||
| 653 | |a Task complexity | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Knowledge management | ||
| 653 | |a Attention | ||
| 653 | |a Community | ||
| 653 | |a Network analysis | ||
| 653 | |a Cognitive tasks | ||
| 653 | |a Distance learning | ||
| 653 | |a Teachers | ||
| 653 | |a Chatbots | ||
| 653 | |a Data analysis | ||
| 653 | |a Large language models | ||
| 653 | |a Knowledge sharing | ||
| 653 | |a Literature reviews | ||
| 653 | |a Collaborative learning | ||
| 653 | |a Education | ||
| 653 | |a Knowledge | ||
| 653 | |a Classroom communication | ||
| 653 | |a Professional training | ||
| 653 | |a Problem solving | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Information processing | ||
| 653 | |a Social interaction | ||
| 653 | |a Academic achievement | ||
| 653 | |a Teacher education | ||
| 653 | |a Educational activities | ||
| 653 | |a Information sharing | ||
| 653 | |a Complex tasks | ||
| 653 | |a Language modeling | ||
| 653 | |a Critical thinking | ||
| 653 | |a Educational Quality | ||
| 653 | |a Learning Analytics | ||
| 653 | |a Learning Activities | ||
| 653 | |a Educational Practices | ||
| 653 | |a Guidance | ||
| 653 | |a Educational Research | ||
| 653 | |a Intelligent Tutoring Systems | ||
| 653 | |a Influence of Technology | ||
| 653 | |a Cognitive Processes | ||
| 653 | |a Learning Experience | ||
| 653 | |a Educational Technology | ||
| 653 | |a Language Acquisition | ||
| 653 | |a Cooperative Learning | ||
| 653 | |a Feedback (Response) | ||
| 653 | |a Communities of Practice | ||
| 653 | |a Knowledge Level | ||
| 653 | |a Electronic Learning | ||
| 653 | |a Instructional Effectiveness | ||
| 653 | |a Language Processing | ||
| 653 | |a Educational Environment | ||
| 653 | |a Course Content | ||
| 653 | |a Learner Engagement | ||
| 653 | |a Educational Facilities Improvement | ||
| 653 | |a Individual Needs | ||
| 700 | 1 | |a Wang, Qi |u Beijing Foreign Studies University, Artificial Intelligence and Human Languages Lab, Beijing, China (GRID:grid.443245.0) (ISNI:0000 0001 1457 2745) | |
| 773 | 0 | |t International Journal of Educational Technology in Higher Education |g vol. 22, no. 1 (Dec 2025), p. 47 | |
| 786 | 0 | |d ProQuest |t Political Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3238556402/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
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