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<p>This study examines predictors of teacher well-being in AI-enhanced learning environments, focusing on stress, burnout, job satisfaction, and overall well-being. Using cross-sectional survey data analyzed with latent profile analysis, we identify distinct teacher well-being profiles and examine factors associated with them, including AI literacy, teaching experience, and perceived leadership support. Results indicate that AI integration is linked to higher well-being for certain teacher groups, suggesting that thoughtful implementation can support sustainable teaching conditions. Methodological limitations, including the cross-sectional design, reliance on self-reported measures, and cultural specificity, constrain causal interpretation and generalizability. Findings underscore the importance of targeted AI training and leadership support to promote teacher well-being.</p>

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Unlocking the Secrets of AI in Education: How AI-Enhanced Learning Environments Shape Teacher Stress, Burnout, Job Satisfaction, and Well-Being – A Hidden Markov Model Approach

  • 期刊论文

  • 发表日期：

    2026年2月25日

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  • Xinyu Liu,
  • Yijia Liu

摘要

This study examines predictors of teacher well-being in AI-enhanced learning environments, focusing on stress, burnout, job satisfaction, and overall well-being. Using cross-sectional survey data analyzed with latent profile analysis, we identify distinct teacher well-being profiles and examine factors associated with them, including AI literacy, teaching experience, and perceived leadership support. Results indicate that AI integration is linked to higher well-being for certain teacher groups, suggesting that thoughtful implementation can support sustainable teaching conditions. Methodological limitations, including the cross-sectional design, reliance on self-reported measures, and cultural specificity, constrain causal interpretation and generalizability. Findings underscore the importance of targeted AI training and leadership support to promote teacher well-being.