Integrating AI into Learning English: University Students’ Perceptions and the Roles of Traditional Methods
As artificial intelligence (AI) continues to become a more vital component in education, understanding how university students are utilizing AI tools for learning English and how they compare to conventional approaches are essential. This study addresses the lack of a comprehensive overview by exploring university students’ experiences and perceptions of using AI tools for learning English, alongside their comparative view with traditional learning methods. A mixed-methods design was employed to collect data from two universities in Ho Chi Minh city, Vietnam. A total of 100 non-English major students participated in answering a questionnaire grounded in the Technology Acceptance Model (TAM), and ten semi-structured interviews were conducted to gain in-depth insights into students’ attitudes toward learning English with AI tools and in conventional classrooms. The findings revealed that the most frequently used AI tools were Google Translate, ChatGPT, Duolingo and Grammarly. The primary purposes for AI uses were to support writing assignments, improve vocabulary and assist with translation. Students generally perceived AI tools as beneficial, with vocabulary, reading and writing self-reported as the most improved skills. Students’ perceived enjoyment was positively rated though moderate concerns were raised regarding the trust of AI-created content and the need for guided support in optimizing AI-based learning experiences. The majority of students expressed a strong desire for teachers to integrate AI into conventional forms of pedagogy to enhance motivation and engagement in learning English. This requires further research on various methods to effectively apply AI in teaching and language skills.
Authors
- Luy Dau
Institutions
- Ho Chi Minh City University of Technology (VN)
Publication Details
- Journal
- Journal of Knowledge Learning and Science Technology ISSN 2959-6386 (online)
- Published
- 2025-05-15
- DOI
- https://doi.org/10.60087/jklst.v4.n2.001
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00