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All Journal Studies in English Language and Education Edcomtech: Jurnal Kajian Teknologi Pendidikan
Ming-Chou Liu
National Dong Hwa University

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Understanding EFL preservice teachers’ intentions and concerns of integrating AI in their future ELT I Putu Indra Kusuma; Sandrotua Bali; Ehsan Namaziandost; Luh Gd Rahayu Budiarta; Ming-Chou Liu; Made Hery Santosa
Studies in English Language and Education Vol. 13 No. 2 (2026)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/siele.v13i2.1068

Abstract

Research shows that English as a foreign language (EFL) in-service teachers are widely implementing AI in English language teaching (ELT). It remains unclear how novice pre-service teachers (PSTs) with limited teaching experience intend to integrate AI into ELT. At the same time, research addressing the concerns of PSTs is scarce. Nonetheless, they represent the forthcoming generation of teachers who will engage with AI in ELT. Given the above gaps, this study aimed to explore the intentions and concerns of EFL PSTs regarding the integration of AI in ELT. This research employed a basic qualitative approach and recruited EFL PSTs enrolled in an English teacher education program at a state university in Indonesia. The data was collected through an open-ended questionnaire and semi-structured interviews. Then, the data from the questionnaire was analyzed using the content analysis method, and the data from the interviews were analyzed using the inductive thematic analysis method. The open-ended questionnaire indicated that EFL PSTs demonstrated significant intentions to use AI. The interview findings also indicated an intention to integrate AI in future instructional design, alongside favorable perceptions of AI for teaching and its application by students in learning contexts. They also expressed both negative and constructive concerns regarding the integration of AI in ELT. Three new implications, such as one theoretical and two practical ones for EFL PSTs, teacher education programs, and policymakers, are drawn for English teacher education programs to enhance EFL PST preparation.
The Role of Generative AI in Advancing Educational Technology Research: A Systematic Review of Qualitative Data Analysis Dedi Aco; Ming-Chou Liu; Harmita Sari
Edcomtech: Jurnal Kajian Teknologi Pendidikan Vol. 11 No. 1 (2026)
Publisher : Universitas Negeri Malang in collaboration with APSTPI and IPTPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um039v11i12026p108-127

Abstract

Large language models (LLMs) are increasingly used for qualitative data analysis; however, questions remain regarding their reliability compared to human coders. Following PRISMA 2020 guidelines, this systematic review synthesizes empirical evidence on the use of generative artificial intelligence for coding interview and focus group data. Of the 1,085 records retrieved from six academic databases between 2020 and 2026, 30 studies met the inclusion criteria. The findings indicate that LLMs, predominantly GPT-4, achieve moderate to substantial thematic agreement with human coders, with Cohen’s kappa values ranging from 0.40 to 0.91 (median 0.72) and accuracy rates between 77% and 96%. Reliability significantly improves with optimized prompting strategies and multi-run ensemble methods. Although LLMs demonstrate exceptional efficiency, reducing analysis time by 80% to 95%, they still face limitations in capturing cultural nuance, interpretive depth, and context-dependent coding. Therefore, current evidence supports the use of LLMs as an augmentation tool rather than a replacement for human researchers. Hybrid human-AI workflows, combining computational efficiency with human interpretive rigor, represent the most promising approach for robust qualitative analysis. For educational researchers, these findings highlight the potential of LLMs to advance qualitative learning analytics by enabling rapid processing of large-scale student data. Ultimately, this hybrid approach allows for deeper insights into technology-enhanced learning environments without sacrificing pedagogical nuance.
Co-Authors Dedi Aco Ehsan Namaziandost Harmita Sari I Putu Indra Kusuma Luh Gd Rahayu Budiarta Made Hery Santosa Sandrotua Bali