This study focuses on the innovative application of large language models (LLMs) in college spoken English teaching. Based on the ever-evolving principles of Human-Computer Interaction (HCI), it systematically analyzes the functional positioning and implementation paths of LLMs in three traditionally core educational roles: language consultant, conversational language partner, and oral production assessor. Through case studies of prompt engineering in teaching scenarios such as listening and speaking training, situational dialogue, and real-time feedback, this paper explores the practical value of LLMs in pronunciation correction, academic oral training, and cross-cultural communication simulation. The research shows that while LLMs still face multiple challenges at the technical, pedagogical, and ethical levels, they can effectively compensate for the traditional shortcomings of oral language instruction at the college level, such as insufficient interaction and delayed feedback.

Large Language Models (LLMs), Human-Computer Interaction (HCI), Prompt Engineering, College English Teaching, Insights from China

1. Barekat, B., & Nobakhti, H. (2014). The effect of authentic and inauthentic materials in cultural awareness training on EFL learners' listening comprehension ability. Theory and Practice in language studies, 4(5), 1058.

2. Chan, C.K.Y.; Tsi, L.H. 2023. The AI Revolution in Education: Will AI Replace or Assist Teachers in Higher Education? arXiv:2305.01185.

3. Chen, C., Koong, C., & Liao, C. (2022). Influences of integrating dynamic assessment into a speech recognition learning design. Educational Technology & Society, 25, 1-14.

4. Demir, O., Keskin, S., & Cinar, M. (2024). The effect of social anxiety on student interactions in asynchronous online discussion forums as mediated by social presence and moderated by anonymity. Journal of Computing in Higher Education, 1-30.

5. Dizon, G., & Tang, D. (2020). Intelligent personal assistants for autonomous second language learning. JALT CALL Journal, 16, 107-120.

6. Edmett, A., et al. (2023). Artificial Intelligence and English Language Teaching. London: British Council.

7. Hai-yan, M. (2014). The Task-Based Teaching of Writing to Big Classes in Chinese EFL Setting. English Language Teaching, 7(3), 63-70.

8. Hegazy, H. M. (2024). A Linguistically Developed Prompt Engineering Parameters Model for Enhancing AI’s Generation of Customized ESL Reading Texts* Dr. Hebatollah MM Hegazy. Faculty of Education Journal Alexandria University, 34(3), 501-566.

9. Hossain, K. I., Ahmed, M. K., & Mahmud, M. S. (2024). A Comprehensive Review on the Impact of Teacher Feedback in Language Development for ESL/EFL Learners. IUBAT Review, 7(1), 218-229.

10. Jia, S., & Lu, Z. (2025). Enhancing EFL Oral Production through Mobile-Assisted Task-Based Language Teaching: A Study in Effectiveness. Journal of Information Technology Education: Research, 24, 012.

11. Jin, L., & Cortazzi, M. (1998). Dimensions of dialogue: Large classes in China. International journal of educational research, 29(8), 739-761.

12. Kumari, P., Swarnalatha, K. S., Naveen, G., Sale, A., Prajwal, M. P., & Siddhardha, K. S. (2025). Challenges and Ethical Considerations of AI in English as a Foreign Language. In Application of AI in the Teaching and Learning of English as a Foreign Language (EFL) (pp. 149-178). IGI Global Scientific Publishing.

13. Li, C., & Lu, L. (2024, November). AI-Enhanced Intercultural Teaching: Investigating Chinese EFL University Teachers’ Technology Readiness and Acceptance of Large Language Models. In International Symposium on Emerging Technologies for Education (pp. 123-134). Singapore: Springer Nature Singapore.

14. Lin, Y. E. (2009). Investigating role-play implementation: A multiple case study on Chinese EFL teachers using role-play in their secondary classrooms.

15. Liu, J., Liu, X., & Yang, C. (2022). A study of college students’ perceptions of utilizing automatic speech recognition technology to assist English oral proficiency. Frontiers in Psychology, 13, 1049139.

16. Liu, M. (2005). Reticence in Oral English Language Classrooms: A Case Study in China. TESL Reporter, 38(1), 1-16.

17. Liu, M. (2006). Anxiety in Chinese EFL students at different proficiency levels. System, 34(3), 301-316.

18. Ou, S. (2023, December). Construction of Oral English Learning Model Based on Artificial Intelligence. In International Conference on Big Data Analytics for Cyber-Physical System in Smart City (pp. 841-854). Singapore: Springer Nature Singapore.

19. Peng, J. E., & Liang, W. (2025). Willingness to communicate with artificial intelligence (AI)? insights from tracking EFL learners’ perceived acceptance and chat output. Computer Assisted Language Learning, 1–27. https://doi.org/10.1080/09588221.2025.2486147.

20. Qin, F., Sun, Q., Ye, Y., & Wang, L. (2025). A multimodal teaching model of college english based on human–computer interaction. International Journal of Human–Computer Interaction, 41(3), 1762-1770.

21. Rassaei, E. (2023). The interplay between corrective feedback timing and foreign language anxiety in L2 development. Language Teaching Research.

22. Roy, D. (2024). Pedagogical Restructuring of Business Communication Courses: AI-Enhanced Prompt Engineering in an EFL Teaching Context. In Artificial Intelligence in Education: The Intersection of Technology and Pedagogy (pp. 247-287). Cham: Springer Nature Switzerland.

23. Savaşçı, M. (2014). Why are some students reluctant to use L2 in EFL speaking classes? An action research at tertiary level. Procedia-social and behavioral sciences, 116, 2682-2686.

24. Shin, Y., Wei, S., & Vanchinkhuu, N. (2025). Digital Plagiarism in EFL Education during the AI Era: A Comparative Study of Perceptions among Learners and Instructors in Korea, Mongolia, and China. LEARN Journal: Language Education and Acquisition Research Network, 18(1), 594-618.

25. Shumin, K. (2002). Factors to consider: Developing adult EFL students' speaking abilities. Methodology in language teaching: An anthology of current practice, 12(35), 204-211.

26. Suban, T. S. (2021). Teaching speaking: activities to promote speaking skills in EFL classrooms. Lectio: Journal of Language and Language Teaching, 1(1), 41-50.

27. Wei, W., Zhao, A., & Ma, H. (2025). Understanding How AI Chatbots Influence EFL learners’ Oral English Learning Motivation and Outcomes: Evidence from Chinese learners. IEEE Access.

28. Xu, Jiajin, Zhao, Chong & Sun, Mingchen. (2024). Applications of Large Language Models in Foreign Language Teaching and Research. Beijing: Foreign Language Teaching and Research Press.

29. Xu, Jiajin, Zhao, Chong. (2024). Roles of Large Language Models in English Language Teaching. Frontiers of Foreign Language Education Research, 7(1), 3-10.

30. Zhang, M. (2024). The Impact of Artificial Intelligence Virtual Oral Tutoring APPs on Chinese Youth’s Anxiety in Oral English learning——Interview Research Based on Users of Artificial Intelligence Speaking Tutoring APPs. Journal of Language, Culture and Education, 1(1), 31-38.

31. Zheng, Y. B., Zhou, Y. X., Chen, X. D., & Ye, X. D. (2025). The influence of large language models as collaborative dialogue partners on EFL English oral proficiency and foreign language anxiety. Computer Assisted Language Learning, 1-27.