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Cited by 5Artificial Intelligence in EducationDigital literacy in educationTeacher Education and Leadership Studies

Investigating preservice STEM teachers’ AI literacy and self-efficacy beliefs: are they ready for AI?

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Aksit, Osman, Akoglu, Kemal, Aydin, Zeynep·2025·Informatics in Education
DOI 10.15388/infedu.2025.22
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Abstract

As artificial intelligence (AI) becomes increasingly integrated into education, preservice science, technology, engineering and mathematics (STEM) teachers must develop both AI literacy and self-efficacy to effectively incorporate AI tools into instruction. This study examined the cognitive and affective orientations of 180 Turkish preservice STEM teachers toward AI, specifically AI literacy, self-efficacy, interest, and attitudes, and identified predictors of AI self-efficacy. Using a performance-based AI literacy test and validated scales, data was analyzed through Rasch modeling and hierarchical regression analysis. While participants demonstrated moderate AI literacy and self-efficacy, the regression results revealed that AI use frequency, interest in AI, and attitudes toward AI significantly predicted AI self-efficacy, whereas demographic, academic, and cognitive factors did not. The findings emphasize the importance of fostering interest and positive attitudes, alongside hands-on experiences with AI tools, in enhancing preservice teachers’ confidence to use AI. The study underscores the need for teacher education programs to integrate both conceptual knowledge and experiential learning opportunities about AI by providing preservice teachers with practical and meaningful activities to explore AI-based tools and applications within their required coursework.

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  • Recent studies suggest AIL is positively associated with TSE beliefs and PI development in AI-integrated educational contexts.

    “This finding is consistent with recent studies (Aksit et al., 2025; Wang and Ye, 2025), which suggest that AIL is positively associated with TSE beliefs and PI development in AI-integrated educational contexts.”

    From AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy (2026) · cites this paper as Aksit et al., 2025

    The Discussion reports prior studies linking AI literacy to AI self-efficacy, which partly supports the self-efficacy portion of the claim. But this paper's Results find that AI literacy was not a significant predictor of AI self-efficacy among its preservice STEM teachers. The cited passages concern AI-specific self-efficacy, not teacher self-efficacy generally, and do not address professional identity development (section 7; section 6; page numbers unavailable). (LLM, conf=0.88)

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Cited by

Sorted by citations

Authors
Aksit, O., Akoglu, K., & Aydin, Z.
Affiliations
Boğaziçi University (TR)
Venue
Informatics in Education
Publisher
Vilnius University Press
Published
2025-12-19
Pages
653-679
ISSN
  • 1648-5831 (ISSN-L)
  • 2335-8971
DOI
10.15388/infedu.2025.22
OpenAlex
W7117466337
Cited by
5
References
57
Citations by year
2026: 5
FWCI
5.24
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Informatics in Education (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Information Systems › Digital literacy in education
  • Social Sciences › Education › Teacher Education and Leadership Studies
Keywords
AI literacy, AI self-efficacy, attitudes toward AI, AI use frequency, teacher professional development, predictors of self-efficacy

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