This study examines the impact of generative artificial intelligence (AI) tools such as ChatGPT, Grammarly, and Quillbot on English language teaching and learning (ELT) in Thai higher education. Despite the rapid adoption of these technologies, limited empirical evidence exists regarding how lecturers and students experience and navigate them. The research aims to (RO1) explore users’ lived experiences with AI tools, (RO2) identify factors that support or hinder AI adoption, and (RO3) develop an extended Technology Acceptance Model (E-TAM) based on empirical data. Semi-structured interviews were conducted with 15 participants, including five lecturers and ten students. Data analysis involved ATLAS.ti, employing open coding, thematic analysis, cross-group comparison, and network visualization. Results indicate that students report more diverse and emotionally salient experiences than lecturers, such as increased confidence, convenience, clearer writing, and greater engagement. However, concerns about accuracy, plagiarism, loss of originality, over-reliance on AI, and unstable internet infrastructure persist. Supportive factors like institutional training and confidence-building facilitate adoption, but only to a limited extent. Based on these findings, an empirically grounded E-TAM is proposed, integrating cognitive, affective, and contextual factors. The study underscores the importance of ethically responsible, pedagogically aligned, and institutionally supported AI integration in ELT.
Toward an Extended technology acceptance model for AI-assisted English learning: A qualitative inquiry into user experiences, supportive conditions, and barriers
Authors
- Weerapon Panurag Department of Information Technology, Faculty of Science and Technology, Rajabhat Maha Sarakham University, Thailand. https://orcid.org/0000-0001-5935-5875
- Duenpen Panurug Department of Information Technology, Faculty of Science and Technology, Rajabhat Maha Sarakham University, Thailand.
- Thada Jantakoon Department of Information and Communication Technology for Education, Faculty of Science and Technology, Rajabhat Maha Sarakham University, Thailand. https://orcid.org/0000-0001-5935-5875
- Wantipa Unarat Department of English Education, Faculty of Education, Nakhon Phanom University, Thailand. https://orcid.org/0009-0001-0052-7879
- Rungfa Pasmala Department of Information and Communication Technology for Education, Faculty of Industrial Education, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand. https://orcid.org/0009-0009-7710-9593
- Panita Wannapiroon Department of Information and Communication Technology for Education, Faculty of Industrial Education, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand. https://orcid.org/0000-0001-8633-5781
- Prachyanun Nilsook Department of Information and Communication Technology for Education, Faculty of Industrial Education, King Mongkut's University of Technology North Bangkok, Bangkok, Thailand. https://orcid.org/0000-0003-3019-3635
