Needs Analysis of an E-Module Integrated with Deep Learning, ESD, and Local Wisdom Approaches Assisted by Canva AI to Facilitate Students' Creative Thinking Skills
DOI:
https://doi.org/10.24036/jipt/vol4-iss2/168Keywords:
E-Module, Keterampilan Berpikir Kreatif, ESD, fisika, Local Wisdom, Canva AI, deep learningAbstract
The development of students’ creative thinking skills in physics learning has not been optimally supported by the teaching materials used in schools, particularly in renewable energy topics. Creative thinking plays an important role in supporting Education for Sustainable Development (ESD) by enabling students to generate innovative solutions to sustainability-related problems. This study aims to analyze the need for developing a Deep Learning-based e-module integrated with ESD, local wisdom, and Canva AI to facilitate students’ creative thinking skills in physics learning. This research employed a Research and Development (R&D) approach using the Four-D (4D) model consisting of the Define, Design, Develop, and Disseminate stages; however, this study was limited to the Develop stage. Data were collected through creative thinking skills tests, student needs analysis questionnaires, ESD questionnaires, and teacher interviews involving 30 tenth-grade students. The data were analyzed using descriptive quantitative techniques. The preliminary findings revealed that students’ creative thinking skills were categorized as low, with an average score of 40.75%, while the originality indicator obtained the lowest score. In contrast, students demonstrated a very high level of readiness for digital learning. These findings indicate the need to develop a Deep Learning-based e-module integrated with ESD, local wisdom, and Canva AI to provide meaningful, contextual, and interactive physics learning that facilitates students’ creative thinking skills.
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Copyright (c) 2026 Resa Dwi Permata Syahendra, Emiliannur, Asrizal, Selma Riyasni

This work is licensed under a Creative Commons Attribution 4.0 International License.
Accepted 2026-07-27
Published 2026-07-27







