The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index.
University students’ intentions to learn artificial intelligence: the roles of supportive environments and expectancy–value beliefs
Open accessAbstract
Despite the importance of artificial intelligence (AI) for university students to thrive in the future workplace, few studies have been conducted to assess and foster their intentions to learn AI. Guided by the situated expectancy–value theory, this study adopted both variable- and person-centered approaches to explore the role of supportive environments and expectancy–value beliefs in fostering university students’ intentions to learn AI. The data were drawn from 494 university students. In Study 1, the variable-centered approach of structural equation modeling showed the critical role of supportive environments and expectancy–value beliefs in promoting students’ intentions to learn AI. In Study 2, the person-centered approach of latent profile analysis identified three subgroups of students based on their levels of supportive environments and expectancy–value beliefs. Consistent with Study 1, students who perceived more supportive environments and higher levels of expectancy–value beliefs had stronger intentions to learn AI. We also documented the influence of study of field, gender, and year level on students' perceptions of supportive environments, expectancy-value beliefs and intentions to learn AI. The implications of these findings in improving students’ intentions to learn AI are discussed.
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“The educational efficacy of these tools varies substantially across disciplines due to varying knowledge structures, pedagogical approaches, and evaluation metrics6.”
From Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes (2025) · cites this paper as [6]
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Cites
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“In addition to the direct effects, we also examined whether expectancy–value beliefs mediated the influence of supportive environments on intentions to learn AI via Sobel tests (MacKinnon et al., 2002).”
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Background
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- Yeung, S. S.., King, R. B., Nalipay, M. J. N., & Cai, Y. (2022). Exploring the interplay between socioeconomic status and reading achievement: An expectancy‐value perspective. British Journal of Educational Psychology, 92(3), 1196-1214. https://doi.org/10.1111/bjep.v92.3 · DOI
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A MI-használati attitűd különbségei pedagógushallgatók és nem pedagógushallgatók között
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Metadata
- Authors
- Wang, F., King, R. B., Chai, C. S., & Zhou, Y.
- Affiliations
- University of Hong Kong (HK)
- Chinese University of Hong Kong (HK)
- Beijing Normal University (CN)
- Corresponding author
- King, Ronnel B.
- Venue
- International Journal of Educational Technology in Higher Education
- Published
- 2023-08-22
- Volume
- 20
- Issue
- 1
- ISSN
- 2365-9440 (ISSN-L)
- OpenAlex
- W4386023368
- Cited by
- 124
- References
- 64
- Citations by year
- 2026: 45 · 2025: 50 · 2024: 27 · 2023: 2
- FWCI
- 31.96
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Educational Technology in Higher Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Education, Achievement, and Giftedness
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Gender Studies › Gender and Technology in Education
- Keywords
- artificial intelligence in education, supportive learning environment, situated expectancy-value theory, university students, student subgroups, gender differences, field of study, year level
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