Enhancing oral English learning through AI: a case study on the impact of AI-driven speaking applications among Chinese university students

Introduction The integration of artificial intelligence (AI) into oral English learning has emerged as a promising solution to challenges such as limited practice opportunities, delayed feedback, and the demand for personalized learning experiences. However, empirical evidence on its effectiveness in university English education remains limited. Methods This study adopted a qualitative case study design to examine the impact of AI-driven English-speaking applications on Chinese university students’ oral proficiency. Six students participated in semi-structured interviews after a 16-week semester, during which they completed structured after-class AI-supported speaking practices and reflective tasks using applications such as Liulishuo. Results Findings indicate that AI applications effectively personalize learning by adapting practice content to individual proficiency levels and providing instant, data-driven feedback. Students reported noticeable improvements in pronunciation, grammar, and fluency, as well as increased motivation and engagement due to interactive features and gamified feedback mechanisms. Discussion Despite these benefits, AI remains limited in replicating the emotional, cultural, and contextual nuances of human communication. Therefore, a blended model that integrates AI tools with traditional teacher-led instruction is recommended. The study offers practical implications for educators and developers seeking to optimize oral English learning through AI-enhanced pedagogical design.