Teachers’ adoption of generative artificial intelligence (GenAI) in educational settings

This study examined various variables associated with teachers’ adoption of Generative Artificial Intelligence (GenAI) technologies for professional use. It aims to identify elements such as AI technology acceptance, AI literacy, and physical access to technology that might be associated with teachers’ professional use of GenAI in educational environments. A total of 270 Israeli teachers completed an online questionnaire, which included demographic data and six Artificial Intelligence (AI) acceptance subscales: social influence, hedonic motivation, performance expectancy, effort expectancy, willingness to use AI, and positive emotions. The questionnaire assessed AI literacy and aspects of physical access from both home and work. Teachers’ professional use of GenAI was significantly associated with the six subscales of the Artificially Intelligent Device Use Acceptance (AIDUA) model, AI literacy, and physical access to technology. Among the AIDUA model subscales, effort expectancy was a significant predictor of AI usage. AI literacy and educational level emerged as strong independent predictors. Better physical access at work was associated with greater AI use among science and technology teachers. This study highlights the need for targeted professional development that enhances AI literacy and addresses the emotional dimensions of AI use among teachers to boost their confidence and competence in AI integration.

Authors

Institutions

Publication Details

Journal
Cogent Education
Published
2026-03-14
DOI
https://doi.org/10.1080/2331186x.2026.2624877
Primary Topic
Educational Leadership and Innovation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Teachers’ adoption of generative artificial intelligence (GenAI) in educational settings

Mor Deshen, Ruben Harari, Noa Aharony
Cogent Education
Educational Leadership and Innovation
article

Teachers’ adoption of generative artificial intelligence (GenAI) in educational settings

Mor Deshen, Ruben Harari, Noa Aharony
article en

Abstract

This study examined various variables associated with teachers’ adoption of Generative Artificial Intelligence (GenAI) technologies for professional use. It aims to identify elements such as AI technology acceptance, AI literacy, and physical access to technology that might be associated with teachers’ professional use of GenAI in educational environments. A total of 270 Israeli teachers completed an online questionnaire, which included demographic data and six Artificial Intelligence (AI) acceptance subscales: social influence, hedonic motivation, performance expectancy, effort expectancy, willingness to use AI, and positive emotions. The questionnaire assessed AI literacy and aspects of physical access from both home and work. Teachers’ professional use of GenAI was significantly associated with the six subscales of the Artificially Intelligent Device Use Acceptance (AIDUA) model, AI literacy, and physical access to technology. Among the AIDUA model subscales, effort expectancy was a significant predictor of AI usage. AI literacy and educational level emerged as strong independent predictors. Better physical access at work was associated with greater AI use among science and technology teachers. This study highlights the need for targeted professional development that enhances AI literacy and addresses the emotional dimensions of AI use among teachers to boost their confidence and competence in AI integration.

Cogent EducationVol. 13(1)
Bar-Ilan University (IL)
Openalex Percentile: Top 21%
Educational Leadership and Innovation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.