Keywords:
artificial intelligence
TAM
learning support
self-efficacy

  • Abstract

    In the wake of the fourth industrial revolution, artificial intelligence is gaining momentum and is widely applied in various aspects of life, particularly education. This study investigates the factors influencing students' use of artificial intelligence (AI) in learning, focusing on students at Ho Chi Minh City University of Industry. The research uses a combination of the technology acceptance model and the theory of planned behavior to examine the relationships between subjective norms, image, job relevance, output quality, result demonstrability, self-efficacy, anxiety, perceived playfulness, perceived enjoyment, perceived ease of use, perceived usefulness, and behavioral intention. Combining these technological models brings new insights into the context of AI that can support or hinder user behavior through bias. The results were then analyzed based on the least squares linear structural model, with 390 students participating in the survey using the stratified sampling approach. The study found that perceived ease of use and usefulness are the most significant factors influencing students' intention to use AI in learning. Subjective norms also play an essential role in shaping students' image and intention to use AI. The research also highlights the importance of self-efficacy, perceived enjoyment, playfulness, output quality, result demonstrability, and job relevance in influencing students' perceptions and use of AI. The findings of this study underscore the need for educational institutions to create a supportive environment that encourages students to use AI in learning. In contrast, AI technology creators need to focus on simplifying the user experience to make AI tools more accessible and easy to use. These practical recommendations of the research can guide policy and design decisions in the field of educational technology. Finally, in place of a conclusion, the study also aims to open up further approaches for AI platforms in academia.

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Duy, N. B. P., Phuong, T. N. M., Chau, V. N. M., Nhi, N. V. H., Khuyen, V. T. M., & Giang, N. T. P. (2024). AI-assisted learning: An empirical study on student application behavior. Multidisciplinary Science Journal, 7(6), 2025275. https://doi.org/10.31893/multiscience.2025275
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