Abstract
As university students have begun to use generative AI daily, their anxiety and attitudes toward AI have changed. Anxiety and attitude toward the learning object may be factors that prevent students from being motivated to learn. This study explores the impact of AI anxiety on first-year university students' willingness to learn and satisfaction with their data science education. We developed questions on impressions of AI from a questionnaire based on the Artificial Intelligence Anxiety Scale and the General Attitudes Towards Artificial Intelligence Scale. The survey was administered to students entering Hiroshima University in 2024. We conducted a factor analysis and identified six factors measuring impressions of AI. We hypothesized two processes as the influence of impressions of AI on university students' education: 1) a three-stage influence of negative attitudes towards AI on their willingness to learn and, consequently, on their teaching on data science; 2) a direct influence of negative attitudes towards AI on their teaching on data science. We tested these assumptions through a covariance structure analysis. We found that negative attitudes towards AI affect the willingness to learn negatively, and the willingness to learn AI positively affects students’ sufficiency in class. This means that the hypothesis 1) is confirmed. We also found that the negative attitude did not affect students’ ability to be sufficient in class. We can show the possibility of an effective class design for students regardless of their attitude toward AI.
Author Information
Yuko Murakami, Hiroshima University, Japan
Tomohiro Inagaki, Hiroshima University, Japan
Paper Information
Conference: IICE2025
Stream: Learning Experiences
This paper is part of the IICE2025 Conference Proceedings (View)
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To cite this article:
Murakami Y., & Inagaki T. (2025) Exploring the Impact of AI Anxiety on First-Year University Students’ Data Science Education ISSN: 2189-1036 – The IAFOR International Conference on Education – Hawaii 2025 Official Conference Proceedings (pp. 317-327) https://doi.org/10.22492/issn.2189-1036.2025.27
To link to this article: https://doi.org/10.22492/issn.2189-1036.2025.27
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