Technology acceptance model for online education: identifying interdisciplinary topics and their evolution based on BERTopic model

As online education technologies rapidly evolve, understanding the dynamics of user acceptance has become a central concern. This study aims to map the intellectual and thematic landscape of Technology Acceptance Model (TAM) research within online education, highlighting key patterns and emerging trends.A total of 6924 articles published between 2020 and 2024 were retrieved from Web of Science. We employed a combination of bibliometric analysis and topic modeling using the BERTopic algorithm to identify collaboration structures and thematic developments.The results reveal four major research themes: learning outcomes, AI-driven pedagogy, professional domain applications, and English language digital learning. Collaborative network analysis highlights regional clustering, particularly among East Asian and Western institutions, with limited cross-cluster exchange. Topic evolution indicates a growing emphasis on artificial intelligence and domain-specific technology adoption.These findings demonstrate TAM's adaptability to new educational technologies and contexts. By tracing topic evolution and collaboration asymmetries, the study provides a dynamic framework for guiding future interdisciplinary research in online education. • A knowledge map of TAM in online education is built using bibliometric and BERTopic models. • 6924 Web of Science articles (2020–2024) are analyzed to track TAM’s evolution and links. • Four clusters emerge: learning outcomes, AI-teaching, digital subjects, and English education. • TAM research peaked in 2022, with rising focus on AI and domain-specific implementations. • TAM's flexibility and guides future tech adoption research in education.