Research article | Open Access
International Journal of Language and Education Research 2025, Vol. 7(1) 116-139
Application of Large-Language-Model-Based Intelligent Study Assistant in College Spoken English Learning: A Case Study of T College
pp. 116 - 139
Publish Date: April 30, 2025 | Single/Total View: 2/3 | Single/Total Download: 2/3
Abstract
This paper explores the application of Large Language Model (LLM)-based intelligent study assistants in college spoken English learning. After a survey conducted to understand student’s needs, the LLM-based intelligent study assistant, Hanna, was integrated into the college’s spoken English curriculum. Students practiced speaking with Hanna after class, and their performances were analyzed by the research team assisted with another LLM, Deepseek. Besides, in order to have a comprehensive grasp of the learning experience, a second survey was conducted to collect student feedback on students’ experience with Hanna.The results showed that Hanna had a positive impact on students’ speaking skills, particularly in grammar, pronunciation and intonation. Most students reported improvements in their speaking abilities and were satisfied with Hanna’s timely feedback. Flexibility was highlighted as a crucial factor in their choice of LLM/AI assistants, and grammar correction was the primary need for betterment. However, limitations were identified in the assistant’s level of intelligence and interpersonal interaction. Additionally, students showed deficiencies in grammatical accuracy and content richness, suggesting the need for more precise grammatical corrections and diverse practice topics. The research concludes that LLM-based intelligent study assistants have significant potential in college spoken English learning but require further developments to address these limitations.
Keywords: Large Language Model; Intelligent Study Assistants; College Spoken English Learning
APA 7th edition
Zhang, Y. (2025). Application of Large-Language-Model-Based Intelligent Study Assistant in College Spoken English Learning: A Case Study of T College. International Journal of Language and Education Research, 7(1), 116-139.
Harvard
Zhang, Y. (2025). Application of Large-Language-Model-Based Intelligent Study Assistant in College Spoken English Learning: A Case Study of T College. International Journal of Language and Education Research, 7(1), pp. 116-139.
Chicago 16th edition
Zhang, Yuan (2025). "Application of Large-Language-Model-Based Intelligent Study Assistant in College Spoken English Learning: A Case Study of T College". International Journal of Language and Education Research 7 (1):116-139.
Chen, S. (2023). Research on the application of large language models in foreign language teaching. Journal of Changchun Normal University, 11, 170-173.
Feng, Z., & Zhang, D. (2024). Large language models in artificial intelligence. Foreign Language Literature, 03, 1-29.
Fredricks, J. A., Filsecker, M., & Lawson, M. A. (2016). Student engagement, context, and adjustment: Addressing definitional, measurement, and methodological issues. Learning and Instruction, 43, 1-4. https://doi.org/10.1016/j.learninstruc.2016.02.002
Gwet, K. L. (2021. Handbook of Inter-Rater Reliability (5th ed.). AgreeStat Analytics.
He, W. (2023). Opportunities and challenges brought by ChatGPT from a linguistic perspective. Journal of Ocean University of China (Social Sciences Edition), 06, 94-103. doi:10.16497/j.cnki.1672-335x.202306009.
Jacob, S., Tate, T., & Warschauer, M. (2023). Emergent AI-assisted discourse: Case study of a second language writer authoring with ChatGPT. arXiv preprint arXiv:2310.10903.
Jiao, J., & Chen, T. (2023). Empowering English teaching with large language models: Four scenarios. Foreign Language Audio-Visual Education, 02, 12-17+106.
Keller, J. M. (2010). Motivational design for learning and performance: The ARCS model approach. Springer.
Long, Y., Luo, H., & Zhang, Y. (2024). Evaluating large language models in analysing classroom dialogue: A case study. npj Science of Learning, (2024)9:60, https://doi.org/10.1038/s41539-024-00273-3
Qiao, H., & Zhao, A. (2023). Artificial intelligence-based language learning: Illuminating the impact on speaking skills and self-regulation in Chinese EFL context. Frontiers in Psychology, 14, 1255594. https://doi.org/10.3389/fpsyg.2023.1255594
Stojanov, A. (2023). Learning with ChatGPT 3.5 as a more knowledgeable other: An autoethnographic study. International Journal of Educational Technology in Higher Education, 20(1), 35. https://doi.org/10.1186/s41239-023-00404-7
Xu, J., Zhao, C., & Sun, M. (2024). Applications of large language models in foreign language teaching and research. Beijing: Foreign Language Teaching and Research Publishing House.
Zhang, Z., & Hong, H. (2023). Foreign language teaching supported by ChatGPT: Empowerment, issues, and strategies. Foreign Language World, 02, 38-44.
