Academic Integrity and Assessment Design in the Era of Generative AI: Perspectives from Vietnamese Universities
DOI:
https://doi.org/10.57125/FED.2026.06.05Keywords:
academic integrity, assessment design, generative AI, higher education, Vietnam.Abstract
The rapid advancement of generative artificial intelligence (GenAI) is beginning to change the way we teach, learn, and assess in higher education. While these technologies present new potential for academic productivity and learning support, they also pose new and serious challenges to academic integrity and the validity of entrenched assessment methods. This study seeks to examine what university lecturers in Vietnam think about the impact of generative AI on academic integrity and how they assess the value of current assessment methods in this new reality. Within a mixed-methods framework, this study employs survey data from 286 lecturers across various academic fields, along with follow-up interviews to deepen the analysis. The primary concerns of lecturers appear to be authorial ambiguity, the misuse of AI in take-home assessments, and the challenges in determining the originality of student work. Traditional essays and writing assessments have also come to be regarded as increasingly vulnerable. Lecturers also overwhelmingly support assessment redesign proposals aimed at incorporating more process-focused assessment, staged submissions, oral defence, in-class assessments, and the reflective disclosure of AI use. Regression analysis also indicates that perceived academic integrity risk, along with institutional support, was the strongest predictor of support for assessment reform. The study concludes that sustainable responses to generative AI involve redesigning assessments rather than relying on AI detection tools. These findings help advance debates on academic integrity and assessment management in the generative AI era, especially in emerging higher education contexts.
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