Integrating Local Wisdom and Generative AI in Ethno-STEM Materials: A Case Study in Border Education

Abstract

The Indonesia-Malaysia border region faces educational challenges in the form of limited learning resources, low digital literacy, and lack of pedagogical innovation. This research aims to develop and evaluate generative AI-assisted Ethno-STEM teaching materials based on Dayak and Malay local wisdom to increase student engagement and creativity. The research uses a mixed methods design with a sequential exploratory model. The qualitative phase was carried out through interviews and observations to explore local wisdom, followed by the preparation of Ethno-STEM-based modules with the integration of AI interactive media, and the quantitative phase involved a limited trial of 30 students of class X and 3 science teachers at SMA Negeri 1 Sajingan Besar. The data were analyzed using descriptive statistics and N-gain calculations. The results showed that the modules were positively rated by students, with 87% stating that they agreed or strongly agreed on the aspects of attractiveness, cultural relevance, and ease of use. The improvement in learning outcomes was also significant, indicated by an average N-gain of 0.91 (high category). The recapitulation of student creativity showed achievements in the category of quite creative, with elaboration obtaining the highest score (55.8%). These findings confirm that the integration of Ethno-STEM and generative AI not only improves conceptual understanding, but also fosters students' critical-creative thinking skills, and is worthy of being recommended for schools in border areas.