Effectiveness of C-R-E-A-T-E Model based on Deep Learning, ESD, and AI to Build High School Students’ Multiple Creativity in Green Chemistry - Wawan Wahyu; Rosi Oktiani (Postgraduate, University of Mataram, 2026)
This research aims to integrate Education for Sustainable Development (ESD) in green chemistry learning through the C-R-E-A-T-E (Connecting-Restructuring-Elaborating-Applying-Tasking-Evaluating) model based on Deep Learning and Artificial Intelligence (DL-AI) to build students' multiple creativity. The methodology used is Design-based Research (DbR) which focuses on the development and evaluation of innovative learning models in a real context. The research subjects consisted of 80 high school students who were selected purposively. The main instruments used are TCOF (Teaching Creativity Observation Form) to measure aspects of compound creativity, as well as observation sheets to assess student engagement and response during the learning process. Data collection techniques include initial and final tests, classroom observations, and limited interviews. The data was analyzed quantitatively using descriptive and inferential statistical tests, as well as qualitative analysis to explore the dynamics of learning. The results show that ESD integration through the DL-AI-based C-R-E-A-T-E model significantly increases students' pluralistic creativity, which includes scientific, social, and ecological creativity. The conclusion, significance, and impact study illustrate that this model also encourages students' active involvement in sustainability issues and data-driven decision-making. This study recommends the application of a similar model in other subjects to support sustainable and transformative 21st century education.