Teachers’ artificial intelligence (AI) literacy: an exploratory study

Abstract This study explores variables associated with teachers’ Artificial Intelligence (AI) literacy, a key competency for effective and responsible AI integration in education. A total of 270 teachers completed an online survey including measures of AI literacy, AI acceptance, computational thinking, AI anxiety, and digital divide. Results revealed that all AI acceptance variables were positively associated with AI literacy, with hedonic motivation and willingness to use AI emerging as the strongest predictors. Computational thinking, AI anxiety, and digital divide also showed significant associations with AI literacy. The findings highlight the central role of teachers’ attitudes and motivational variables over technical and demographic variables. The study contributes to the understanding of how teachers engage with AI technologies and provides practical implications for designing professional development programs that enhance AI literacy and reduce barriers to AI adoption in educational contexts.

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Publication Details

Journal
Smart Learning Environments
Published
2026-01-21
DOI
https://doi.org/10.1186/s40561-026-00433-5
Citations
1
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
44.01
Controls
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article

Teachers’ artificial intelligence (AI) literacy: an exploratory study

Ruben Harari, Mor Dehen, Noa Aharony
1 citations
Smart Learning Environments
Teaching and Learning Programming
44.01
article

Teachers’ artificial intelligence (AI) literacy: an exploratory study

Ruben Harari, Mor Dehen, Noa Aharony
article en
1 citations

Abstract

Abstract This study explores variables associated with teachers’ Artificial Intelligence (AI) literacy, a key competency for effective and responsible AI integration in education. A total of 270 teachers completed an online survey including measures of AI literacy, AI acceptance, computational thinking, AI anxiety, and digital divide. Results revealed that all AI acceptance variables were positively associated with AI literacy, with hedonic motivation and willingness to use AI emerging as the strongest predictors. Computational thinking, AI anxiety, and digital divide also showed significant associations with AI literacy. The findings highlight the central role of teachers’ attitudes and motivational variables over technical and demographic variables. The study contributes to the understanding of how teachers engage with AI technologies and provides practical implications for designing professional development programs that enhance AI literacy and reduce barriers to AI adoption in educational contexts.

Smart Learning EnvironmentsVol. 13(1)
Bar-Ilan University (IL)
Quality Education
Openalex Percentile: Top 1%
Teaching and Learning Programming
44.01
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