Utilization of Artificial Intelligence in STEM education: A study in Vietnam
The integration of Artificial Intelligence (AI) into education has transformed teaching and learning methodologies worldwide, particularly in Science, Technology, Engineering, and Mathematics (STEM) disciplines. This study explores the utilization of AI in STEM education in Vietnam, focusing on its implementation, benefits, and challenges. Through a mixed-methods approach, including surveys and interviews with educators, students, and policymakers, the research investigates how AI tools-such as intelligent tutoring systems, adaptive learning platforms, and data analytics-are being adopted in Vietnamese STEM classrooms. Findings reveal that AI enhances personalized learning experiences, improves student engagement, and supports teachers in delivering complex concepts effectively. In Vietnam, where STEM education is a national priority, AI applications are increasingly bridging gaps in resource availability and teacher training. However, challenges such as limited technological infrastructure, high implementation costs, and a lack of AI literacy among educators hinder widespread adoption. The study also highlights cultural and systemic factors influencing AI integration, including resistance to change and disparities between urban and rural educational settings. This research contributes to the global discourse on $A I$ in education by offering insights into its potential and limitations within a developing country context, providing recommendations for policymakers and educators to optimize AIdriven STEM education in Vietnam.
Authors
- Bùi Văn Hồng (ORCID: https://orcid.org/0000-0002-0690-2027)
- Phuong Bich Tran (ORCID: https://orcid.org/0000-0003-2119-998X)
- Phan Nguyen Truc Phuong
- Nguyen Viet Trung
- Dinh Van De
- Le Hoang Phuoc Hien
Institutions
- Ho Chi Minh City University of Education (VN)
- Ho Chi Minh City University of Technology and Engineering (VN)
- Ho Chi Minh City University of Technology (VN)
- VNU University of Science (VN)
Publication Details
- Published
- 2025-07-30
- DOI
- https://doi.org/10.1109/istem-ed65612.2025.11129400
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69