AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy.
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This study investigates how cognitive, affective, and behavioral variables related to artificial intelligence (AI) build AI self-efficacy among university students. Based on these variables, we identify three meaningful student groups, which can guide educational initiatives. We recruited 1465 undergraduate and graduate students from the United States, the United Kingdom, and Germany and measured their AI self-efficacy, AI literacy, interest in AI, attitudes towards AI, and AI use. Using a path model, we examine the correlations and paths among these variables. Results reveal that AI usage and positive AI attitudes significantly predict interest in AI, which in turn and together with AI literacy, enhance AI self-efficacy. Moreover, using Gaussian Mixture Models, we identify three groups of students: 'AI Advocates,' 'Cautious Critics,' and 'Pragmatic Observers,' each exhibiting unique patterns of AI-related cognitive, affective, and behavioral traits. Our findings demonstrate the necessity of educational strategies that not only focus on AI literacy but also aim to foster students' AI attitudes, usage, and interest to effectively promote AI self-efficacy. Furthermore, we argue that educators who aim to design inclusive AI educational programs should take into account the distinct needs of different student groups identified in this study.
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Attitudes and actual use of AI strongly predict interest, which together with baseline AI literacy builds self-efficacy.
“attitudes, usage, and interest interact to build AI self-eÿcacy”
From Human-in-the-loop assessment with AI: implications for teacher education in Ibero-American universities (2025) · cites this paper as Bewersdorff et al., 2025
Interest together with AI literacy enhances AI self-efficacy.
“whichtogether with AI literacy-enhances AI self-eÿcacy”
From Human-in-the-loop assessment with AI: implications for teacher education in Ibero-American universities (2025) · cites this paper as Bewersdorff et al., 2025
AI use and positive attitudes significantly predict interest.
“A large multi-country study fi nds that AI use and positive attitudes significantly predict interest”
From Human-in-the-loop assessment with AI: implications for teacher education in Ibero-American universities (2025) · cites this paper as Bewersdorff et al., 2025
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- Authors
- Bewersdorff, A., Hornberger, M., Nerdel, C., & Schiff, D. S.
- Affiliations
- Technical University of Munich (DE)
- Purdue University West Lafayette (US)
- Corresponding author
- Bewersdorff, Arne
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 8
- Pages
- 100340
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4405095658
- Cited by
- 148
- References
- 67
- Citations by year
- 2026: 88 · 2025: 60
- FWCI
- 55.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- Google Research
- Bundesministerium für Bildung und Forschung (16DHBKI051)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Gender Studies › Gender and Technology in Education
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Keywords
- AI self-efficacy, AI literacy, attitudes toward AI, artificial intelligence use, university students, student typology, artificial intelligence in education
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