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This study systematically maps the intellectual structure and thematic evolution of technology acceptance research in education, with a specific focus on how human behavior is represented in technology-mediated learning contexts. Using BERTopic, a neural embedding-based topic modeling approach, we analyzed 4,788 journal articles and review papers indexed in Scopus between 1981 and 2025. The analysis identified 42 distinct research topics, which were further examined through contextual taxonomies, topic similarity analysis, hierarchical clustering, and temporal trend analysis. The findings show that technology acceptance research in education has historically concentrated on digital learning environments, particularly e-learning, mobile learning, and learning management systems. However, temporal analyses indicate a relative stabilization in these domains, characterized by a gradual recalibration of their proportional representation within the scholarly literature in recent years. In contrast, emerging topics such as generative artificial intelligence, AI chatbots, ChatGPT, metaverse, and immersive learning technologies exhibit strong upward trends, indicating a clear shift from platform-oriented acceptance toward interaction with intelligent and adaptive systems. From a human behavior perspective, the distinct clustering of AI-related topics suggests that scholarly attention toward their acceptance dynamics is evolving differently than traditional digital platforms. The results indicate that contemporary technology acceptance increasingly involves behavioral and psychological dimensions such as trust formation, ethical judgment, perceived agency, transparency, and experiential engagement, which extend beyond the explanatory scope of classical acceptance constructs such as perceived usefulness and perceived ease of use. By providing a large-scale, data-driven mapping of technology acceptance research in education, this study contributes to the literature on human behavior and human–technology interaction by clarifying how acceptance-related themes have evolved in response to generative AI and immersive learning environments. The findings offer a conceptual roadmap for future research aimed at understanding and modeling human behavior in AI-mediated educational settings.
Ursavaş et al. (Fri,) studied this question.