Teacher Involvement in Developing Sustainable Education Materials for AI Integration in Green Energy Education

Synthesis: Riandi et al. (2026) examine the factors influencing teachers' ability to integrate AI into science and green energy education within the framework of Education for Sustainable Development (ESD). Using a structural equation model based on the TPACK framework with 122 in-service teachers, they find that only two constructs significantly influence teachers' AI-integration capability: use of AI in science and green energy and involvement in developing ESD-aligned teaching materials. In contrast, abstract AI knowledge and attitudes toward AI did not show significant effects — underscoring the importance of practical engagement and contextual experience over abstract cognition.

Key Findings

1. Practical use and material development drive integration. Teachers' actual use of AI in science/green energy and their involvement in developing ESD-based materials significantly predicted AI-integration capability.
2. Abstract knowledge and attitudes were not significant. AI Knowledge and Attitudes Toward AI showed no significant effect, likely due to measurement limitations and the constrained role of abstract cognition relative to hands-on experience.
3. TPACK as the analytic lens. The study grounds its model in the TPACK framework, emphasizing the integration of technological, pedagogical, and content knowledge for green energy education.
4. Teacher involvement matters for ESD. Engaging teachers in developing ESD-aligned materials is a concrete pathway to building their capacity to integrate AI meaningfully.

Relevance

This is a teacher-education perspective on the knowledge base's Sustainability concept page, showing how to build the educator capacity needed for AI-integrated sustainability education. It connects to Teaching, Technological Pedagogical Content Knowledge (TPACK), and the practical dimension of using AI for sustainability education in K-12 and green energy contexts.

What this means for practice

• Instructors. Build AI-integration capability by using AI on science and green energy tasks rather than by studying it: actual use of AI in science and green energy significantly predicted integration capability, while abstract AI knowledge did not.
• Instructors. Co-develop the materials you will teach, since involvement in developing ESD-aligned teaching materials was the second significant predictor of integration capability.
• Faculty developers. Organize professional development around producing ESD-based resources with AI in the loop, treating teacher involvement in material development as the capacity-building mechanism rather than a byproduct of training.
• Administrators. Do not expect attitude-improvement campaigns to shift integration capability: attitudes toward AI showed no significant effect in the model.

Limitations

• 122 in-service teachers recruited by purposive sampling from a single professional-development program, which the authors say may have introduced selection bias toward teachers already open to innovation.
• The questionnaire used a dichotomous (0/1) scale, which limited data variability and sensitivity and weakened the predictive power of the PLS-SEM model used.
• The AI Knowledge construct showed weak reliability and validity, evidenced by low Cronbach's alpha and composite reliability values that the authors say could destabilize estimates in the structural model.
• No AI training was provided before the survey, so the design captures an exploratory baseline only and cannot show whether capability changes over time or across disciplines.

Citation

Riandi, R., Ismail, I., Kaniawati, I., Sopandi, W., Rostikawati, D. A., Hamka, D., & Suhendar, S. (2026). Teacher Involvement in Developing Sustainable Education Materials for AI Integration in Green Energy Education. Scientific Reports, 15.