Designing Benchmarking Frameworks for Teacher Support in AI-Integrated STEM Education Curriculum: Insights from Cambodia - Cendana, Wiputra; Im, Keun; Alim, Evan Kurnia (The Indonesian Institute of Science and Technology Research, 2025)
This paper proposes a benchmarking mechanism to evaluate and enhance teacher support systems in AI and digital STEM-integrated curricula in Cambodia. It addresses the critical need for educators to incorporate AI effectively, using a mixed-research approach that combines qualitative text analysis with quantitative clustering techniques. Employing a mixed-methods approach, textual data from Cambodian teachers were collected via surveys and interviews, preprocessed (tokenization, stop-word removal, stemming), and analyzed using TF-IDF vectorization and K-means clustering. These themes form the framework's dimensions, measured by contextually relevant metrics, aiming to foster a supportive environment for teachers. Recommendations include partnerships, flexible learning pathways, and pilot programs, contributing to global best practices while addressing local educational challenges. The resulting framework dimensions (training, infrastructure, and policy integration) are measured by context-specific metrics. Findings underscore the necessity of tailored training, equitable resource allocation, and collaborative learning communities in empowering teachers to effectively integrate AI and digital technologies into STEM education. Recommendations include partnerships with tech firms, flexible learning pathways, and iterative policy adjustments based on teacher feedback.