This research analyzes the incorporation of artificial intelligence (AI) into sustainability education, utilizing constructivist and inquiry-based learning methodologies. The objective is to investigate how AI-driven teaching modules might improve students' comprehension of sustainability ideas while promoting critical thinking and problem-solving abilities.
In what ways might AI-driven technologies improve sustainability education utilizing constructivist and inquiry-based approaches?
What are the ramifications of employing AI to customize sustainability education inside a European framework?
The aim of this project is to develop and assess an AI-driven teaching module that utilizes real-world sustainability issues to include students in active learning. This module will tailor itself to the specific needs of individual learners, including interactive information and scenarios that promote collaborative exploration of solutions.
Theoretical Framework: The research is based on constructivist learning theory, which highlights the active creation of knowledge by learners. Inquiry-based learning functions as an auxiliary approach, fostering student-led investigation and resolution of problems. The use of AI improves these methods by offering individualized learning trajectories and prompt feedback.
The research corresponds with the European Green Deal and the Digital Education Action Plan by tackling environmental issues using new teaching approaches. This cross-European research seeks to promote intercultural conversation and disseminate best practices among the participating nations. The study's results will enhance the worldwide dialogue on education's function in combating climate change and promoting sustainability.

This research used a mixed-methods methodology: Participants: Primary-Secondary school students from three European nations, chosen to represent a varied array of cultural and scholastic backgrounds. Intervention: An AI-driven module intended to showcase practical sustainability concerns, like waste management and the use of renewable energy. Data Acquisition: Quantitative: Administration of pre- and post-assessments to evaluate knowledge acquisition and critical thinking abilities. Qualitative: Student comments, focus groups, and instructor interviews to provide insights regarding engagement and usability. Behavioral Data: AI interaction records for the analysis of learning patterns and module efficacy. Analysis: Quantitative data will undergo statistical analysis to evaluate knowledge acquisition. Qualitative data will undergo theme coding to discern trends and insights.

The research predicts that the AI-driven sustainability module will: Improve students' comprehension of sustainability concepts by contextualizing them in engaging, real-world scenarios. Promote critical thinking and collaborative problem-solving abilities via inquiry-based activities. Exhibit the capability of AI in customizing education to address varied learner requirements. Expected outcomes comprise actionable insights for the integration of AI into sustainability education and recommendations for the scaling of this approach across European schools. The findings will enhance current discussions regarding the use of technology in educational innovation.

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