Development and validation of the artificial intelligence learning intention scale (ailis) for university students
Open accessAbstract
As artificial intelligence (AI) permeates almost all aspects of our lives, university students need to acquire relevant knowledge, skills, and attitudes to adapt to the challenges it poses. This study reports the development and validation of a scale called the Artificial Intelligence Learning Intention Scale (AILIS). AILIS was designed to measure the different factors that shape university students’ behavioral intentions to learn about AI and their AI learning. We recruited 907 Chinese university students who answered the survey. The scale is comprised of 9 factors that are categorized into various dimensions pertaining to epistemic capacity (AI basic knowledge, programming efficacy, designing AI for social good), facilitating environments (actual use of AI systems, subjective norms, access to support and technology), psychological attitudes (resilience, optimism, personal relevance), and focal outcomes (behavioral intention to learn AI, actual learning of AI). Reliability analyses and confirmatory factor analyses indicated that the scale has acceptable reliability and construct validity. Structural equational modeling results demonstrated the critical role played by epistemic capacity, facilitating environments, and psychological attitudes in promoting students’ behavioral intentions and actual learning of AI. Overall, the findings revealed that university students express a strong intention to learn about AI, and this behavioral intention is positively associated with actual learning. The study contextualizes the theory of planned behavior for university AI education, provides guidelines on the design of AI curriculum courses, and proposes a possible tool to evaluate university AI curriculum.
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Fostering AI literacy competencies is considered a crucial mission for higher education in preparing students for an AI-driven future.
“Fostering these competencies is now considered a crucial mission for Higher Education (HE) in preparing students for an AI-driven future (Chai et al., 2024; Ng et al., 2022; Miao and Cukurova, 2024; Schmohl et al., 2023).”
From The AI literacy heptagon: A structured approach to AI literacy in higher education (2026) · cites this paper as Chai et al., 2024
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Zhang, L., & Xu, J. (2025) · The Internet and Higher Education · doi:10.1016/j.iheduc.2024.100978 · Cited by 168 · see citing works
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The AI literacy heptagon: A structured approach to AI literacy in higher education
Hackl, V., Müller, A. E., & Sailer, M. (2026) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2026.100540 · Cited by 41 · see citing works
“Fostering these competencies is now considered a crucial mission for Higher Education (HE) in preparing students for an AI-driven future (Chai et al., 2024; Ng et al., 2022; Miao and Cukurova, 2024; Schmohl et al., 2023).”
- Authors
- Chai, C. S., Yu, D., King, R. B., & Zhou, Y.
- Affiliations
- Chinese University of Hong Kong (HK)
- Beijing Normal University (CN)
- Corresponding author
- Zhou, Ying
- Venue
- Sage Open
- Publisher
- SAGE Publications
- Published
- 2024-04
- Volume
- 14
- Issue
- 2
- ISSN
- 2158-2440 (ISSN-L)
- OpenAlex
- W4394718136
- Cited by
- 69
- References
- 23
- Citations by year
- 2026: 49 · 2025: 19 · 2024: 1
- FWCI
- 18.22
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- SAGE Open (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Beijing Social Science Foundation Project (22JYA005)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Information Systems › Information Systems Education and Curriculum Development
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- university students, artificial intelligence in education, epistemic capacity, facilitating environment, psychological attitudes, theory of planned behavior, AI curriculum design, scale development and validation
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