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
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · cited 5×
“Our study provides evidence that both supportive environments and expectancy–value beliefs are crucial factors in fostering students’ intentions to learn AI, which is consistent with previous research (Kelly et al., 2023; Teo & Zhou, 2014; Venkatesh et al., 2003).”
BackgroundMethodResultInfluentialA Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066 · cited 2×
“…gain more experience with technology, the influence of significant others is expected to diminish, with individuals relying more on their own past experiences to shape their perceptions of technology, rather than the opinions of others (Venkatesh & Davis, 2000; Venkatesh & Morris, 2000).”
BackgroundResultSensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance
Chen, F. F. (2007) · Structural Equation Modeling: A Multidisciplinary Journal · doi:10.1080/10705510701301834 · Cited by 12,217 · cited 1×
“According to the criteria recommended by Chen (2007), ΔCFI ≤ − 0.01, ΔRMSEA ≤ 0.015, and ΔSRMR ≤ 0.03 were the criteria for metric invariance, while ΔCFI ≤ − 0.01, ΔRMSEA ≤ 0.015, and ΔSRMR ≤ 0.01 were the criteria for scalar invariance.”
InfluentialA comparison of methods to test mediation and other intervening variable effects
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.83 · Cited by 9,406 · cited 1×
“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).”
BackgroundExpectancy-value theory of achievement motivation
Wigfield, A., & Eccles, J. S. (2000) · Contemporary Educational Psychology · doi:10.1006/ceps.1999.1015 · Cited by 7,383 · cited 2×
“Existing research has indicated that students’ expectancy and value beliefs are domain-specific, demonstrating that high levels of expectancy and value beliefs in one subject may not necessarily translate to high levels in another subject (Wigfield & Eccles, 2000).”
BackgroundZawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · cited 2×
“However, existing research on AI education has mostly focused on the curriculum and technical aspects (Salas-Pilco & Yang, 2022; Zawacki-Richter et al., 2019).”
BackgroundStructural Equation Modeling: Concepts, Issues, and Applications.
Smith, M. D., & Hoyle, R. H. (1996) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2988418 · Cited by 6,875
(1995) · Cited by 4,008
Predicting and Changing Behavior
Fishbein, M., & Ajzen, I. (2011) · doi:10.4324/9780203838020 · Cited by 2,807
Venkatesh, V., & Morris, M. G. (2000) · MIS Quarterly · doi:10.2307/3250981 · Cited by 2,780 · cited 1×
“…gain more experience with technology, the influence of significant others is expected to diminish, with individuals relying more on their own past experiences to shape their perceptions of technology, rather than the opinions of others (Venkatesh & Davis, 2000; Venkatesh & Morris, 2000).”
Background
Research works printed but not linked: 1
- 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
Cited by
Sorted by citations
An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI
Chan, C. K. Y., & Zhou, W. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00284-4 · Cited by 192
“…widely studied in educational technology (e.g., Ifinedo, 2018) but often within general contexts such as AI learning intention (Chai et al., 2021; Wang et al., 2023) or in non-GenAI settings, for example, AI-enabled automatic scoring applications (Fu et al., 2020) and AI teaching assistants (Kim…”
BackgroundFalebita, O. S., & Kok, P. J. (2024) · Journal for STEM Education Research · doi:10.1007/s41979-024-00132-1 · Cited by 154
“Undergraduates with a high degree of technological self-efficacy are more likely to explore new AI tools as they are released into the public domain (Kong et al., 2021; Obenza et al., 2024; Wang et al., 2023).”
Conceptualising and cultivating Critical GAI Literacy in doctoral academic writing
Ou, A. W., Khuder, B., Franzetti, S., & Negretti, R. (2024) · Journal of Second Language Writing · doi:10.1016/j.jslw.2024.101156 · Cited by 93
Molefi, R. R., Ayanwale, M. A., Kurata, L., & Chere-Masopha, J. (2024) · Computers and Education Open · doi:10.1016/j.caeo.2024.100191 · Cited by 125
Wang, K., Cui, W., & Xue, Y. (2025) · Behavioral Sciences · doi:10.3390/bs15020165 · Cited by 139
“Building upon this foundation, B. Wang et al. (2023) examined the intricate interplay between digital and AI technologies by leveraging Wilson et al.’s (2015) technological-cognitive-ethical model model alongside the KSAVE framework within the Information and Communication Technologies (ICT) domain.”
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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