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All Journal Journal of Innovative Technologies in Learning and Education An-Nasyi'in: Jurnal Ilmu Pendidikan
Ninh Nguyen Tuan
Thanh Do university
Computer Science & IT Education Social Sciences Other

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Exploring IT Students’ Use of Generative AI for Technical Vocabulary Learning: A Qualitative Study Ninh Nguyen Tuan
An-Nasyi'in: Jurnal Ilmu Pendidikan Vol. 2 No. 1 (2026)
Publisher : An-Nasyi'in: Jurnal Ilmu Pendidikan

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Abstract

This study explores how Information Technology (IT) students use generative Artificial Intelligence (AI) tools to support technical vocabulary learning in an English for Specific Purposes (ESP) context. Adopting a qualitative design, the study involved 20 third-year IT students at a private university in Hanoi, Vietnam. Data were collected through semi-structured interviews and analyzed using thematic analysis. The findings indicate that students use generative AI as part of a multimodal learning ecosystem, combining tools such as chatbots and coding assistants to explain technical terms, generate examples, and simplify complex concepts. AI was found to enhance conceptual understanding and promote learner autonomy. However, concerns regarding over-reliance and the accuracy of AI-generated content were also reported. The study highlights the importance of guided AI integration and the development of critical AI literacy in ESP instruction.
BETWEEN ASSISTANCE AND CHEATING: ENGLISH LECTURERS’ EVALUATION OF AI-SUPPORTED STUDENT WRITING Ninh Nguyen Tuan; Nguyet Pham Thi
Journal of Innovative Technologies in Learning and Education Vol. 3 No. 2 (2026): Journal of Innovative Technologies in Learning and Education - September 2026
Publisher : Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/1me3xf95

Abstract

The rapid emergence of generative artificial intelligence (AI) tools such as ChatGPT has transformed writing practices in higher education and raised important questions regarding authorship, assessment, and academic integrity. Although previous studies have explored the benefits and challenges of AI-assisted writing, limited research has examined how lecturers evaluate student writing that involves AI support. This study investigates how English lecturers conceptualise the boundary between acceptable AI-assisted writing and academic misconduct and explores the factors influencing their assessment and grading decisions in Vietnamese higher education. A qualitative design was employed. Semi-structured interviews were conducted with twenty English lecturers from three universities in Hanoi. The interviews were analysed using reflexive thematic analysis, with coding and theme development cross-checked by two researchers. The findings indicate that lecturers do not view AI use in student writing as inherently unethical. Rather, they understand AI use along a continuum, distinguishing between AI as a learning support and AI as a substitute for students’ intellectual effort and authorship. Assessment decisions were influenced by perceptions of student agency, transparency in AI use, evidence of learning, ethical considerations, institutional expectations, and professional experience. Rather than relying primarily on AI-detection tools, lecturers emphasised students’ ability to explain, justify, and reflect on their writing processes. The study concludes that assessing AI-supported writing is a pedagogical and interpretative practice rather than a purely technical or rule-based process. The findings highlight the need for context-sensitive assessment frameworks, clearer institutional guidance, and professional development to support lecturers in navigating AI-related assessment challenges.
Co-Authors Nguyet Pham Thi