The Utilization of Generative AI (Esisbot) for Advancing Comprehension of Indigenous Science in Higher Education

Abstract

This study explores innovations in generative AI named Esisbot to support students in improving their understanding and insights into indigenous knowledge. In science, mathematics, and technology education, Indigenous knowledge is often overlooked and receives little attention. This topic is rarely considered in research on technology policy and the use of technology in education. Advanced technologies such as Chat GPT and generative AI are currently being considered. However, higher education policymakers have not yet prioritized improving the capabilities and role of generative AI in building databases of Indigenous scientific material for science, technology, engineering, and mathematics education is urgent needed. The methods of this research through empirical studies and use mixed methods in three universities as an example such as University Tadulako, UIN Walisongo Semarang, and University of Sarjanawiyata Tamansiswa. The data was obtained through indepth interviews wth 5 students in each location, classroom observation, and interviews with 3 lecturers and distributive questionnaires to 30 students each. While, the method of analysis uses descriptive analysis and inferential analysis, interpretation and triangulation. The statistical analysis using a paired sample t-test (t(29) = 6.781, p < 0.001) further supports the conclusion that Esisbot played a significant role in advancing learning outcomes. The findings indicate that Esisbot significantly benefits students by enhancing their comprehension of indigenous knowledge and improving their quality and capacity in understanding STEM within a local knowledge framework