The Impact of ChatGPT Voice on Non-Majored Students’ Willingness to Communicate in a Vietnamese University
ChatGPT represents a significant advancement in language education because it engages in natural and contextually appropriate conversations. This technology offers EFL students enhanced opportunities to practice speaking English in a relaxed environment. While most studies on AI-powered chatbots in higher education have demonstrated their positive effects on students' willingness to communicate (WTC), there has been a lack of research specifically focusing on ChatGPT Voice (CV) in Vietnamese universities. This study explores the impact of CV’s conversational interface on the WTC of EFL students at a university in the Mekong Delta region of Vietnam. In three months, 30 non-English major students participated in ten speaking activities facilitated by CV, supplemented by ten worksheets. These activities were conducted in various formats: group work (four activities), pair work (four activities), and individual sessions (two activities). The impact of CV on students' WTC was assessed using a mixed-methods approach, including 2 questionnaires, one before and one after the intervention, and a semi-structured interview with seven students following the intervention. Preliminary findings indicate that the use of CV could enhance students' WTC. Among the factors influencing WTC, self-perceived competence emerged as the most significant factor affecting students’ WTC (β = 0.630), surpassing the engagement and anxiety factors. The study recommends the integration of CV in EFL classrooms to stimulate students' motivation and improve their speaking skills.
Authors
- Huynh Thuy Uyen Pham (ORCID: https://orcid.org/0009-0008-7205-1436)
- Xuan Tran (ORCID: https://orcid.org/0009-0008-5272-244X)
- Tran Thuy Lieu Dinh (ORCID: https://orcid.org/0009-0001-6246-5562)
Institutions
- Văn Hiến University (VN)
- Ho Chi Minh City Open University (VN)
- Dong Thap University (VN)
Publication Details
- Journal
- AsiaCALL Online Journal
- Published
- 2025-05-03
- DOI
- https://doi.org/10.54855/acoj.2516116
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 0.00