Skip to content
Open access

BETWEEN ASSISTANCE AND CHEATING: ENGLISH LECTURERS’ EVALUATION OF AI-SUPPORTED STUDENT WRITING

Sep 2026 · Journal of Innovative Technologies in Learning and Education · 0 citations

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.

Read PDF

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.