Experimenting with AI-based mobile applications to improve student engagement in ornamental plant learning in rural Indonesian schools

Haris Setyawan () and Dwijoko Purbohadi ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 3, 2333-2343

Abstract: This study aims to optimize student involvement in learning about ornamental plants using technology. Learning experiments were conducted in rural elementary schools in Indonesia. We used an artificial intelligence (AI)-based mobile application to help students recognize ornamental plants via cell cameras and obtain information directly. This study employed a quasi-experimental design with pre-and post-test methods. Ninety-three students in Grades 5 and 6 participated in the study. An application was developed using the MobileNetV2 convolutional neural network model. For one month, students worked in small groups to search for and identify ornamental plants in the surrounding environment. This study demonstrates that AI-based applications can improve student engagement and understanding. The results showed an increase of 35% in comprehension after they used the application. Interviews and observations indicated that the students were more enthusiastic about learning because they received instant feedback and a more interactive learning experience. We identified several obstacles during the experiment, including limited digital literacy and infrastructure readiness. These obstacles can hinder the implementation of AI applications in real learning. We recommend that schools conduct capacity building for teachers and develop adequate infrastructure to further implement this technology.

Keywords: AI Education; Mobile Learning; MobileNetV2; Ornamental Plants; Student Engagement. (search for similar items in EconPapers)
Date: 2025
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