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Cited by 191Artificial Intelligence in EducationGender and Technology in EducationDigital literacy in education

The DOI matches its Crossref record; 63 of the 67 references it cites were found in a registry or an index.

AI advocates and cautious critics: How AI attitudes, AI interest, use of AI, and AI literacy build university students’ AI self-efficacy.

Open access
Bewersdorff, Arne, Hornberger, Marie, Nerdel, Claudia, Schiff, Daniel S.·2025·Computers and Education: Artificial Intelligence
DOI 10.1016/j.caeai.2024.100340
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Abstract

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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Crossref · 10.1016/j.caeai.2024.100340

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Citations

Cites

Show all 67 references

Research works printed but not linked: 4

  1. Bandura, 1994, Self-efficacy, Vol. 4, 71
  2. Hong, 2022, I was born to love AI: The influence of social status on AI self-efficacy and intentions to use AI, International Journal of Communication, 16, 172
  3. Hu, 2023, ChatGPT sets record for fastest-growing user base - analyst note, Reuters
  4. Pajares, 1997, Current directions in self-efficacy research, Vol. 10, 1

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Related

Metadata

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
ORCID
Venue
Computers and Education: Artificial Intelligence
Publisher
Elsevier BV
Published
2025-06
Volume
8
Pages
100340
ISSN
2666-920X (ISSN-L)
DOI
10.1016/j.caeai.2024.100340
OpenAlex
W4405095658
Cited by
148
References
67
Citations by year
2026: 88 · 2025: 60
FWCI
55.65
Type
journal article
Language
en
Open access
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
  • Social Sciences › Education › Artificial Intelligence in Education
  • Social Sciences › Gender Studies › Gender and Technology in Education
  • Social Sciences › Education › 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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