Empowering oral English teachers with AI to improve digital and blended teaching competencies - Wang, Ru; Mokhtar, Mazlin Mohamed (Edelweiss Applied Science and Technology, 2025)
The rapid advancement of artificial intelligence (AI) is reshaping the landscape of oral English education, placing new demands on educators to cultivate advanced digital teaching competencies. In response to this challenge, the present study develops and implements an AI-empowered blended teaching framework that integrates the BOPPPS (Bridge-In, Objectives, Pre-assessment, Participatory Learning, Post-assessment, and Summary) model with the PADD (Presentation, Assimilation, Discussion, and Demonstration) model. This hybrid framework aims to enhance the instructional effectiveness of English teachers through structured pedagogical and technological integration. A 12-week training program was conducted involving 72 college English teachers, from which 56 valid responses were analyzed. Using SPSS statistical tools, paired-samples t-test results revealed statistically significant improvements: AI tool adoption increased by 25%, classroom engagement rose by 47%, and teachers’ self-confidence in using AI technologies improved by 45%. These findings indicate that the proposed AI training framework is not only effective but also scalable and adaptable for professional development in oral English teaching. The framework proposed in this study offers a replicable and scalable model for teacher professional development.