Enhancing Students’ Interest in Fuel Cell Education Using Augmented Reality through Smartphone Applications in Malaysia
Year: 2025
Type: article
Study design: Clinical Trial
Abstract: The rapid advancement of fuel cell systems, driven by technological progress, has led to increased global adoption. However, in Malaysia, exposure to renewable energy technologies among children remains inadequate, resulting in limited awareness of their importance. This study addresses this gap by leveraging augmented reality (AR) through a mobile application, SmartSelFuel, to enhance students’ interest in fuel cell education. Using the ADDIE instructional design model, the study began with an analysis of children’s understanding and challenges in grasping fuel cell concepts. The SmartSelFuel application was tested on 77 nine-year-old children using a quantitative research approach to evaluate improvements in knowledge acquisition and awareness of renewable energy. Results showed a remarkable 96.2% increase in students’ understanding and knowledge after interacting with the application. Statistical analysis confirmed a significant impact, with a p-value < 0.05. This study introduces SmartSelFuel as an innovative learning model that integrates AR to create an engaging and interactive educational experience. The application not only improved children’s cognitive performance but also significantly increased their interest in renewable energy innovations and heightened awareness of its critical role in sustainable development. These findings underscore the transformative potential of AR-based mobile applications in fostering renewable energy education and inspiring future generations to embrace sustainable practices.
Source: Jurnal Kejuruteraan
Authors: Institut Sel Fuel, Universiti Kebangsaan Malaysia, Sahriah Basri, Siti Umiara Zakaria, Institut Sel Fuel, Universiti Kebangsaan Malaysia, Mismisuraya Meor Ahmad, Umawathy Techanamurthy, Siti Kartom Kamarudin, Institut Sel Fuel, Universiti Kebangsaan Malaysia
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408945135