Enterprise AI Analysis
Teacher involvement in developing sustainable education materials for AI integration in green energy education
Authors: Riandi Riandi, Ismail Ismail, Ida Kaniawati, Wahyu Sopandi, Diana Ayu Rostikawati, Defrizal Hamka & Suhendar Suhendar
DOI: 10.1038/s41598-025-34422-4 | Published: 08 January 2026
This study explores how teacher involvement in developing sustainable education materials influences their capacity to integrate AI into green energy education, revealing critical insights for effective professional development.
Executive Impact: Key Findings for AI Integration
This research provides critical insights for educational leaders and policymakers aiming to enhance AI integration in sustainable education.
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Variance Explained in AI Integration Ability (R²)
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Direct Impact of Practical AI Use on Integration
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Direct Impact of ESD Material Involvement
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Teachers Surveyed
Practical engagement and active participation in material development are stronger predictors of AI pedagogical readiness than cognitive or affective factors alone. This shift in focus can drive more effective teacher training programs.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Overall AI Integration
Practical AI Use (Green Energy)
ESD Material Development
Knowledge & Attitudes (Impact)
Research Methodology
Overall AI Integration Capability
The study found that 55.4% of the variance in teachers' AI Integration Ability is explained by the combination of four key factors: AI Knowledge, Attitude Toward AI in ESD, Use of AI in Science Green Energy, and ESD Teaching Material involvement. This indicates a moderate predictive power for the model.
0.554
R² Value for AI Integration Ability (Variance Explained)
This highlights that while multiple factors contribute, the overall readiness for AI integration in sustainable education is significantly influenced by a blend of cognitive, affective, and practical engagements.
Impact of Practical AI Use in Green Energy Education
Hypothesis testing revealed that the Use of AI in Science and Green Energy had a significant influence on AI Integration Ability (β = 0.391, t = 3.063, p < 0.01), with a moderate effect size (f² = 0.180). This suggests that direct engagement with AI tools in applied contexts is a strong predictor of integration.
Case Study: Experiential Learning with AI in Green Energy
A group of teachers in the study actively utilized AI tools, such as data analytics platforms and simulation software, to develop interactive lessons on renewable energy sources (solar panel optimization, wind turbine efficiency). Through this hands-on experience, they not only enhanced their technical proficiency but also developed creative pedagogical strategies to introduce complex AI concepts to students in a relevant and engaging manner. This practical application directly correlated with their increased ability to integrate AI into their teaching practices effectively.
Key Takeaway: Practical, context-based application of AI significantly boosts teachers' confidence and capability in integrating AI into green energy education.
Influence of Teacher Involvement in ESD Material Development
Teacher involvement in developing ESD-based teaching materials also had a significant positive effect on AI Integration Ability (β = 0.236, t = 1.936, p < 0.05), with a moderate effect size (f² = 0.127). This indicates that active participation in content creation enhances pedagogical readiness for AI.
Case Study: Co-creating Sustainable AI Curricula
Teachers who collaborated to design new ESD-focused teaching modules, incorporating AI as a tool for data analysis on environmental impact or for personalizing learning paths on sustainability topics, showed higher AI integration capabilities. This process involved critical reflection on how AI could serve specific educational objectives within sustainability, fostering a deeper, more contextual understanding of AI's pedagogical potential beyond mere technical knowledge.
Key Takeaway: Engaging teachers in the creation of AI-enhanced, sustainability-focused educational content builds deeper pedagogical competence and accelerates AI integration.
Limited Impact of AI Knowledge and Attitudes
In contrast to practical application and material development, AI Knowledge (H1: p = 0.466) and Attitude Toward AI in ESD (H2: p = 0.343) did not show statistically significant effects on AI Integration Ability. This surprising finding suggests that cognitive and affective factors alone are insufficient predictors without practical engagement.
| Factor | Influence on AI Integration Ability (Beta/Significance) | Key Finding |
|---|---|---|
| AI Knowledge | β = 0.011 (p = 0.466) - Insignificant |
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| Attitude Toward AI in ESD | β = 0.058 (p = 0.343) - Insignificant |
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| Practical AI Use (Green Energy) | β = 0.391 (p < 0.01) - Significant, Moderate Effect |
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| ESD Material Development Involvement | β = 0.236 (p < 0.05) - Significant, Moderate Effect |
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These results highlight the need for teacher training to move beyond theoretical knowledge and general attitudes, focusing instead on practical, experience-based models for integrating AI in sustainability education.
Research Methodology Flow
This study employed a quantitative, correlational survey design using Partial Least Squares Structural Equation Modelling (PLS-SEM) to explore relationships among AI-related constructs and sustainable pedagogical practices among 122 in-service teachers.
Enterprise Process Flow
Quantitative approach
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Survey method
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Exploring constructs related to the integration of AI in sustainable pedagogical practices
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Partial Least Squares Structural Equation Modeling (PLS-SEM)
Data was collected via an online questionnaire and analyzed to assess validity, reliability, and the strength of relationships between latent constructs based on the TPACK, TAM, and constructivist frameworks.
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Estimated Annual Savings
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Annual Hours Reclaimed
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AI Integration Roadmap: From Insight to Impact
Based on this analysis, here's a generalized timeline for implementing AI integration strategies in your educational institution to boost teacher capability.
Phase 1: Needs Assessment & Pilot Program Design (1-3 Months)
Conduct a detailed assessment of existing teacher competencies, current curriculum, and technological infrastructure. Design a pilot professional development program focusing on hands-on AI application in specific subject areas (e.g., green energy education) and collaborative material development for ESD.
Phase 2: Experiential Training & Resource Development (3-6 Months)
Implement the pilot program, emphasizing active use of AI tools for pedagogical tasks rather than theoretical knowledge. Support teachers in co-creating AI-enhanced ESD teaching materials, facilitating peer learning and reflection. Gather initial feedback and performance data.
Phase 3: Scaling & Integration (6-12 Months)
Refine training programs based on pilot results. Expand successful models across more departments and teachers, focusing on sustained practical engagement. Develop clear guidelines and provide ongoing technical and pedagogical support for AI integration within the curriculum.
Phase 4: Continuous Improvement & Innovation (Ongoing)
Establish mechanisms for continuous evaluation of AI integration effectiveness. Foster a culture of innovation, encouraging teachers to experiment with new AI tools and share best practices. Regularly update materials and training to keep pace with AI advancements and evolving educational goals.
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