How Chinese EFL teachers worried about AI? A study on AI anxiety, working engagement and teacher resilience

Purpose The present study seeks to explore the level of AI anxiety among Chinese EFL teachers, with consideration for age, gender, years of teaching, experience of blended teaching and educational background as potential influential variables. The aim is to gain a better understanding of the various factors that contribute to individuals' attitudes towards AI integration into language education. This study also seeks to understand whether AI anxiety predicts Chinese EFL teachers' work engagement and teacher resilience. Also, how is the association between work engagement and resilience under the context of the AI era? Design/methodology/approach This study aimed to reveal the relationships between EFL teachers' AI anxiety, work engagement and teacher resilience. It used a quantitative method. The convenient sample and snowball sample were used in the study. The participants of the study are 206 EFL teachers from different levels of educational institutions and cities in China. Findings Chinese EFL teacher AI anxiety is low to moderate level. AI anxiety manifestation was remarkably consistent regardless of teachers' gender, blended teaching experience, educational background or years of professional experience. The AI anxiety of Chinese EFL teachers was not significantly correlated with work engagement or teacher resilience, suggesting that apprehensions about artificial intelligence may not currently disrupt educators' job-related motivation or adaptive capacities. Research limitations/implications While our preregistered analysis provided strong evidence for the null hypothesis, future studies might explore nonlinear relationships or moderator effects using larger, cross-cultural samples. Cross-sectional design precludes causal inferences, and longitudinal studies could track how AI anxiety evolves with increased classroom integration. Moderators unexplored variables like EFL teacher digital literacy or institutional leadership support might clarify boundary conditions for AI anxiety's effects. For future studies, sample specificity should also be concerned. Generalizability may be limited if participants had low AI exposure. Replication in tech-intensive settings (e.g. schools adopting AI tutors) is warranted. Future studies should also explore different types of AI anxiety (e.g. ethical concerns vs. fear of obsolescence). Originality/value While AI anxiety did not emerge as a salient factor in this study, its negligible role may reflect contemporary conditions rather than inherent irrelevance. As educational AI adoption accelerates, monitoring its psychological impact – particularly alongside protective factors like engagement – will be critical to supporting teacher well-being.

Authors

Institutions

Publication Details

Journal
Artificial Intelligence in Education
Published
2026-05-08
DOI
https://doi.org/10.1108/aiie-08-2025-0204
Primary Topic
Digital literacy in education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

How Chinese EFL teachers worried about AI? A study on AI anxiety, working engagement and teacher resilience

Siyu Wang, Fang Liu
Artificial Intelligence in Education
Digital literacy in education
article

How Chinese EFL teachers worried about AI? A study on AI anxiety, working engagement and teacher resilience

Siyu Wang, Fang Liu
article en

Abstract

Purpose The present study seeks to explore the level of AI anxiety among Chinese EFL teachers, with consideration for age, gender, years of teaching, experience of blended teaching and educational background as potential influential variables. The aim is to gain a better understanding of the various factors that contribute to individuals' attitudes towards AI integration into language education. This study also seeks to understand whether AI anxiety predicts Chinese EFL teachers' work engagement and teacher resilience. Also, how is the association between work engagement and resilience under the context of the AI era? Design/methodology/approach This study aimed to reveal the relationships between EFL teachers' AI anxiety, work engagement and teacher resilience. It used a quantitative method. The convenient sample and snowball sample were used in the study. The participants of the study are 206 EFL teachers from different levels of educational institutions and cities in China. Findings Chinese EFL teacher AI anxiety is low to moderate level. AI anxiety manifestation was remarkably consistent regardless of teachers' gender, blended teaching experience, educational background or years of professional experience. The AI anxiety of Chinese EFL teachers was not significantly correlated with work engagement or teacher resilience, suggesting that apprehensions about artificial intelligence may not currently disrupt educators' job-related motivation or adaptive capacities. Research limitations/implications While our preregistered analysis provided strong evidence for the null hypothesis, future studies might explore nonlinear relationships or moderator effects using larger, cross-cultural samples. Cross-sectional design precludes causal inferences, and longitudinal studies could track how AI anxiety evolves with increased classroom integration. Moderators unexplored variables like EFL teacher digital literacy or institutional leadership support might clarify boundary conditions for AI anxiety's effects. For future studies, sample specificity should also be concerned. Generalizability may be limited if participants had low AI exposure. Replication in tech-intensive settings (e.g. schools adopting AI tutors) is warranted. Future studies should also explore different types of AI anxiety (e.g. ethical concerns vs. fear of obsolescence). Originality/value While AI anxiety did not emerge as a salient factor in this study, its negligible role may reflect contemporary conditions rather than inherent irrelevance. As educational AI adoption accelerates, monitoring its psychological impact – particularly alongside protective factors like engagement – will be critical to supporting teacher well-being.

Artificial Intelligence in EducationVol. 2(1)
University of North Dakota (US), Greensboro College (US), Guilin University of Electronic Technology (CN)
Quality Education
Openalex Percentile: Top 2%
Digital literacy in education
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.