Exploring the Competitive and Collaborative Use of Generative AI in STEM Education for Diverse and Inclusive Applications: A Case Study of Bilingual Education for Taiwan's Minority Ethnic Groups

Abstract

This study explores the competitive and collaborative use of generative artificial intelligence (GenAI) in enhancing bilingual STEM education for Taiwan's minority ethnic groups. Amid Taiwan’s multicultural landscape, shaped by the New Southbound Policy, immigrant children face significant linguistic and cultural challenges, hindering their academic progress, particularly in STEM fields. Traditional methods, such as volunteer interpretation, bilingual educators, and machine translation tools, have proven insufficient for addressing the complexity of specialized STEM terminology and cross-cultural nuances. To overcome these challenges, this paper proposes an innovative educational system that integrates generative AI, utilizing a multi-agent framework where large-scale language models engage in both competitive and cooperative learning. By combining peer review reinforcement learning (PRRL) and iterative collaborative fusion (ICF), the system aims to provide high-quality, contextually accurate, and culturally sensitive bilingual instruction. The system dynamically adapts to the students’ needs, enhancing their learning experience and improving their performance in STEM subjects. Through continuous iteration and model collaboration, the framework ensures the delivery of precise, professional educational content that supports inclusivity and academic equity. This research contributes to fostering diverse and innovative talent in STEM, promoting greater educational accessibility in Taiwan’s multicultural society.

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