Development of Guided Inquiry Learning Using AI-Based Material Classification Media on the Concept of Sound Waves

Abstract

Understanding sound waves remains a challenge for high school students due to their abstract nature and the limitations of teacher-centered learning that relies on verbal explanations and simple illustrations. To address this, this study developed a guided inquiry learning model integrated with artificial intelligence (AI)-based material classification media to enhance students' conceptual understanding of sound waves. This study employed a research and development (R&D) approach with a modified Borg & Gall model, consisting of five phases: needs analysis, design, development, implementation, and evaluation. Data were collected through classroom observations, teacher and student questionnaires, interviews, and concept testing, and analyzed using validity, practicality, feasibility, and effectiveness assessments. The AI ​​media prototype was designed to classify materials based on their acoustic properties, such as sound absorption, reflection, and transmission, enabling students to conduct virtual explorations that are difficult to observe directly. The results showed that the developed model met validity and practicality criteria based on expert review and user feedback. Furthermore, the effectiveness test demonstrated a significant increase in students' conceptual understanding, with normalized gain scores falling in the moderate to high category. This study concludes that integrating AI-based material classification into guided inquiry learning provides a promising strategy for bridging the gap between abstract physics concepts and students' learning experiences. These findings highlight the potential of AI as an innovative medium in physics education to foster scientific thinking and enhance conceptual mastery.