Exploring the Impact of Resistance to <scp>AI</scp> , Cultural Openness and Collaboration on <scp>STEM</scp> Teachers' Professional Development: A Structural Equation Modelling Study

ABSTRACT As artificial intelligence (AI) technologies become increasingly integrated into educational settings, understanding the factors that influence teachers' acceptance or resistance to AI is critical, particularly in the STEM education sector. Despite growing interest in AI in education, few studies have examined the psychological and cultural determinants of teachers' resistance to AI and how these factors impact their professional development. This study addresses this gap by investigating the roles of resistance, cultural attitudes and collaborative practices among Chinese STEM teachers. A quantitative, survey‐based approach was adopted, and data were collected from 475 Chinese STEM teachers in January 2025. Structural Equation Modelling (SEM) was employed, alongside descriptive statistics, correlation analysis, regression and factor analysis using SPSS and AMOS, to examine the relationships among resistance, cultural attitudes, collaboration and professional development. Findings indicate that resistance to change is negatively associated with professional development, accounting for 37% of the variance, whereas cultural openness and collaboration emerged as strong positive predictors, explaining 49% and 58% of the variance, respectively. These results underscore the importance of addressing psychological barriers and fostering supportive, culturally responsive environments to facilitate AI integration and enhance teacher development. The study offers actionable implications for policymakers and educational leaders seeking to design effective AI‐related training programs and support systems for STEM educators.

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Publication Details

Journal
European Journal of Education
Published
2025-11-04
DOI
https://doi.org/10.1111/ejed.70311
Primary Topic
E-Learning and COVID-19
Type
article
Field-Weighted Citation Impact
0.00
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article

Exploring the Impact of Resistance to AI , Cultural Openness and Collaboration on STEM Teachers' Professional Development: A Structural Equation Modelling Study

Bo Sun, Asta Rauduvaitė, Zhiyu Yao, Yadian Du
European Journal of Education
E-Learning and COVID-19
article

Exploring the Impact of Resistance to AI , Cultural Openness and Collaboration on STEM Teachers' Professional Development: A Structural Equation Modelling Study

Bo Sun, Asta Rauduvaitė, Zhiyu Yao, Yadian Du
article en

Abstract

ABSTRACT As artificial intelligence (AI) technologies become increasingly integrated into educational settings, understanding the factors that influence teachers' acceptance or resistance to AI is critical, particularly in the STEM education sector. Despite growing interest in AI in education, few studies have examined the psychological and cultural determinants of teachers' resistance to AI and how these factors impact their professional development. This study addresses this gap by investigating the roles of resistance, cultural attitudes and collaborative practices among Chinese STEM teachers. A quantitative, survey‐based approach was adopted, and data were collected from 475 Chinese STEM teachers in January 2025. Structural Equation Modelling (SEM) was employed, alongside descriptive statistics, correlation analysis, regression and factor analysis using SPSS and AMOS, to examine the relationships among resistance, cultural attitudes, collaboration and professional development. Findings indicate that resistance to change is negatively associated with professional development, accounting for 37% of the variance, whereas cultural openness and collaboration emerged as strong positive predictors, explaining 49% and 58% of the variance, respectively. These results underscore the importance of addressing psychological barriers and fostering supportive, culturally responsive environments to facilitate AI integration and enhance teacher development. The study offers actionable implications for policymakers and educational leaders seeking to design effective AI‐related training programs and support systems for STEM educators.

European Journal of EducationVol. 60(4)
Vytautas Magnus University (LT), Xinxiang University (CN), Henan Medical University (CN)
Openalex Percentile: Top 65%
E-Learning and COVID-19
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