Literature Review: Integrating Teachable Machine into Project-Based Learning (PBL) Models to Optimize Understanding of Physics Concepts - Meutia Elsafira Munggarani (Jurnal Inovasi Global, 2026)
The urgency of mastering Artificial Intelligence (AI) literacy, set against the difficulty students face in understanding abstract physics concepts without visual representation, presents a real challenge in the digital era. Therefore, this study aims to examine the potential and effectiveness of integrating Teachable Machine (TM) within the Project-Based Learning (PjBL) model to optimise the understanding of physics concepts in the topics of vibration, waves, and sound. This study was conducted using a systematic literature review approach encompassing 36 peer-reviewed scientific articles published between 2020 and 2025. The results of the analysis found that the TM platform has proven to function as effective digital scaffolding for visualising abstract physical data into concrete pattern representations. Furthermore, this technological collaboration with the PjBL model is empirically shown to enhance intrinsic motivation, sharpen computational thinking skills, and reduce students' physics misconceptions through active inquiry-based activities. Overall, the synergy between no-code AI and PjBL offers a transformative pedagogical strategy that not only deepens students' understanding of science concepts, but also successfully transforms the role of learners into independent, agentic learners.