Artificial intelligence-based learning recommendation system to promote critical and creative thinking on junior high school physics topics

Abstract

This study aims to design a learning recommendation system to support the development of critical and creative thinking skills in junior high school (SMP) physics topics. The system is designed to help students gain a deeper and more integrated understanding of scientific concepts, enabling them to relate physics to real-life contexts and other disciplines, such as biology and chemistry. The study employs a Research and Development (R&D) method, which includes needs analysis, system design, data collection and processing, and recommendation system development. The dataset consists of 7,524 learning objectives across various science topics selected per the 7th-grade SMP curriculum. The recommendation system is developed using a content-based filtering approach, allowing for personalised recommendations of learning materials and activities tailored to students’ learning profiles. The results indicate that this system has the potential to serve as an effective tool in supporting integrated physics learning in junior high school, as well as in fostering thinking skills essential for 21st-century challenges.