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<p>The integration of Artificial Intelligence (AI) in education holds transformative potential, particularly in advancing Education for Sustainable Development (ESD) and green energy literacy. However, empirical evidence is lacking on how teacher involvement in ESD material development influences their capacity to integrate AI into pedagogical contexts. This study aims to examine the relationships among AI knowledge, attitudes toward AI, involvement in material development, and teachers’ ability to apply AI in green energy education. Employing a quantitative correlational design, data were collected from 122 in-service teachers engaged in professional development programs. A structured questionnaire measured five constructs, which were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicated that teachers’ practical use of AI in science and green energy learning (β = 0.391, <i>p</i> &lt; 0.01, f<sup>2</sup> = 0.180) and their involvement in developing ESD-based teaching materials (β = 0.236, <i>p</i> &lt; 0.05, f<sup>2</sup> = 0.127) significantly influenced their AI integration capabilities. In contrast, AI knowledge and attitudes toward AI did not show statistically significant effects. These findings suggest that experiential engagement with AI and active participation in sustainability-focused content development are stronger predictors of AI pedagogical readiness than cognitive or affective factors alone. This study underscores the importance of shifting teacher training toward context-based, practice-oriented models to enhance technological competence in sustainability education.</p>

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Teacher involvement in developing sustainable education materials for AI integration in green energy education

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Riandi Riandi,
  • Ismail Ismail,
  • Ida Kaniawati,
  • Wahyu Sopandi,
  • Diana Ayu Rostikawati,
  • Defrizal Hamka,
  • Suhendar Suhendar

摘要

The integration of Artificial Intelligence (AI) in education holds transformative potential, particularly in advancing Education for Sustainable Development (ESD) and green energy literacy. However, empirical evidence is lacking on how teacher involvement in ESD material development influences their capacity to integrate AI into pedagogical contexts. This study aims to examine the relationships among AI knowledge, attitudes toward AI, involvement in material development, and teachers’ ability to apply AI in green energy education. Employing a quantitative correlational design, data were collected from 122 in-service teachers engaged in professional development programs. A structured questionnaire measured five constructs, which were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicated that teachers’ practical use of AI in science and green energy learning (β = 0.391, p < 0.01, f2 = 0.180) and their involvement in developing ESD-based teaching materials (β = 0.236, p < 0.05, f2 = 0.127) significantly influenced their AI integration capabilities. In contrast, AI knowledge and attitudes toward AI did not show statistically significant effects. These findings suggest that experiential engagement with AI and active participation in sustainability-focused content development are stronger predictors of AI pedagogical readiness than cognitive or affective factors alone. This study underscores the importance of shifting teacher training toward context-based, practice-oriented models to enhance technological competence in sustainability education.