Understanding GenAI-Assisted English Learning Among College Students: A TAM- and SRL-Based Interview Study

XingYu Long; MinMin Tu; ZiJun Cheng; MeiYi Shi

doi:10.63313/LLCS.9132

Authors

XingYu Long College of Foreign Languages, The University of Shanghai for Science and Technology, Shanghai 200000, China Author
MinMin Tu College of Foreign Languages, The University of Shanghai for Science and Technology, Shanghai 200000, China Author
ZiJun Cheng College of Foreign Languages, The University of Shanghai for Science and Technology, Shanghai 200000, China Author
MeiYi Shi College of Foreign Languages, The University of Shanghai for Science and Technology, Shanghai 200000, China Author

DOI:

https://doi.org/10.63313/LLCS.9132

Keywords:

Generative AI, self-regulated learning, technology acceptance

Abstract

This study examines how college students use generative AI for English self-regulated learning. 16 undergraduates were interviewed using semi-structured protocols. Data were analyzed via hybrid thematic analysis using NVivo 11. The results reveal three usage frequencies: frequent (31.25%), occa-sional (43.75%), and on-demand (25%). Translation (87.5%) and writing assis-tance (75%) were primary uses, while listening training was unused. Although all participants valued AI for improving efficiency and output quality, over half ques-tioned its long-term developmental impact and expressed concerns about shal-low content and over-reliance. AI effectively supported monitoring and reflection (62.5% engagement) but rarely aided learning planning (18.75%). Key barriers included inaccurate outputs (62.5%) and challenges in formulating precise prompts (56.25%). Attitudes toward academic integrity varied, with most con-sidering the line between assistance and cheating as purpose-dependent. This study offers nuanced insights into generative AI integration in language learning and suggests implications for optimizing AI-supported educational practices.

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