International Journal of Educational Technology in Higher Education最新文献

Artificial Intelligence (AI) chatbots are increasingly becoming integral components of the digital learning ecosystem. As AI technologies continue to evolve, it is crucial to understand the factors influencing their adoption and use among students in higher education. This study is undertaken against this backdrop to explore the behavioral determinants associated with the use of the AI Chatbot, ChatGPT, among university students. The investigation delves into the role of ChatGPT’s self-learning capabilities and their influence on students’ knowledge acquisition and application, subsequently affecting the individual impact. It further elucidates the correlation of chatbot personalization with novelty value and benefits, underscoring their importance in shaping students’ behavioral intentions. Notably, individual impact is revealed to have a positive association with perceived benefits and behavioral intention. The study also brings to light potential barriers to AI chatbot adoption, identifying privacy concerns, technophobia, and guilt feelings as significant detractors from behavioral intention. However, despite these impediments, innovativeness emerges as a positive influencer, enhancing behavioral intention and actual behavior. This comprehensive exploration of the multifaceted influences on student behavior in the context of AI chatbot utilization provides a robust foundation for future research. It also offers invaluable insights for AI chatbot developers and educators, aiding them in crafting more effective strategies for AI integration in educational settings.

Emotions play a crucial role in the learning process, yet there is a scarcity of studies examining emotion dynamics in problem-solving with fine-grained data and advanced tools. This study addresses this gap by investigating the emotional trajectories during self-regulated learning (SRL) phases (i.e., forethought, performance, and self-reflection) among 47 medical students utilizing an intelligent tutoring system. Real-time facial expressions were analyzed through recurrence quantification analysis alongside an examination of electrodermal activities (EDA) across the SRL phases. The findings reveal that emotion stability varied across SRL phases, with students exhibiting more stable emotions during the performance phase. Compared to the forethought and self-reflection phases, students had less frequent and lower intensity of emotional arousal in the performance phase. Moreover, we found that students with better performance demonstrated more stable emotions in the forethought phase, less stable emotions in the self-reflection phase, and a higher level of emotional arousal in the self-reflection phase. These insights highlight the temporal and dynamic nature of emotions in SRL, offering methodological and educational implications for leveraging facial expressions and EDA to monitor and enhance students’ emotional experience during problem-solving.

Utilising emancipatory approaches to educational technology in higher education allows welcoming creative and artistic modes of inquiry. This article presents two methods, a virtual makerspace and a guided fantasy story that were applied in a project concerned with rewilding higher education pedagogy. It is argued that the methods encouraged curiosity and care to address diversity and inclusion. They afforded mindfulness of individual needs and welcomed explorations of new directions that challenged potential biases (gender, race, disability or professionality). The article illustrates how these two methods may offer a safe space to dream and imagine educational spaces.