Increasing student performance and satisfaction in a speaking course through AI-supported active learning activities

Abstract

This study investigates the impact of active learning techniques on oral communication skills and course satisfaction among English as a foreign language (EFL) teacher candidates in Türkiye. Using a mixed-methods approach, the researcher collected quantitative data from oral exams and qualitative data from a postcourse satisfaction survey. Results revealed statistically significant improvement in oral communication performance, with AI supported active learning activities showing a positive influence. Students reported increased confidence, fluency, and motivation to speak English. Particularly effective activities included student-chosen role-play scenarios, speaking in front of peers, and consistent language practice opportunities. The approach proved successful in both improving academic performance and fostering positive attitudes toward English speaking by helping these  prospective teachers overcome communication anxiety and develop greater comfort with self-expression.