Integrating AI Teaching Assistants and Knowledge Graphs in College English Listening and Speaking Education: A Study of Evaluation and Practice

This study investigate the integration of AI teaching assistants and knowledge graphs in college English listening and speaking education, and evaluate its effect. This research adopt quasi-experimental design, which involves 100 non-native English-speaking undergraduates from different disciplines. Experimental group and control group were compared for four weeks. Through pre- and post-tests of students’ English listening and speaking ability, students’ satisfaction survey and qualitative feedback, the results of the research shows that the integration of AI teaching assistants and knowledge graphs significantly improve students’ listening and speaking ability, increase students’ engagement, and gained high satisfaction. Despite its effectiveness, the research also pointed out some challenges, such as the consistent improvement of AI's accuracy, and student workload brought by technology-enhanced tasks. The future research could explore broader demographic in order to generalize the results, investigate the long-term effect, as well as study the applicability of this technology under different education background. In conclusion, the integration of AI teaching assistants and knowledge graphs present great potential and value in improving college English education, and can provide more interactive and personalized study experience.