Development of a Chemistry Teaching Module on Chemical Bonding Based on STEM Integrated with Artificial Intelligence (AI) to Improve Students’ Critical Thinking Skills - Risqah Amaliah Kasman; Muhammad Fuad; Muhammad Ilham (Postgraduate, University of Mataram, 2025)
This research aims to develop a chemistry teaching module on chemical bonding based on the STEM approach integrated with Artificial Intelligence (AI) to improve students’ critical thinking skills. The study employs a Research and Development (R&D) method using the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The development stages include needs analysis, module design, expert validation, limited trials, and classroom implementation. The module integrates STEM-based learning with AI features such as interactive simulations, adaptive exercises with instant feedback, and chatbot explanations. The validation results show that the module is categorized as valid to very valid with an average score of 3.36–3.80. Limited trials indicate very positive teacher responses and 93.14% of students agreed that the module was effective. Learning outcomes demonstrate that class mastery exceeded 80%, with individual minimum scores above 75. The N-Gain score reached 74.72% in the high category, and the t-test showed a significant difference (p < 0.05) between pretest and posttest. These findings conclude that the developed STEM-AI integrated module is valid, practical, and effective for improving students’ critical thinking skills in learning chemical bonding.