Using ChatGPT to Evaluate the Methodological Components of Research Proposals: An Experimental Study on Undergraduate English Majors in Vietnam

Abstract

Artificial Intelligence is increasingly applied in education, but its effectiveness in evaluating research methodologies remains underexplored. This study examines the intra- and inter-rater reliability of ChatGPT-4o, employing zero-shot learning, in assessing 37 research proposals from English majors at Saigon University, Vietnam, focusing on Research Title, Questions, Hypotheses, Paradigm, Design, and Techniques. A quantitative quasi-experimental design was used, with two evaluation groups: Control (module lecturers) and Experimental (ChatGPT-4o). ChatGPT-4o followed a structured zero-shot prompt set, with a researcher-designed five-point rubric and the How to Research book uploaded for reference to evaluate each proposal twice. The lecturers evaluated proposals independently, discussed and finalized scores. Data collected were analyzed using Quadratic Cohen’s weighted Kappa. Results showed moderate to high intra-rater reliability and moderate inter-rater reliability in straightforward areas, but the machine struggled with abstract criteria requiring deeper reasoning, such as evaluating title relevance and the justification of paradigm and design. These findings highlight the limitations of AI in fully capturing the complexities of research methodologies. However, ChatGPT-4o may be a reliable tool in contexts with clear rubrics and minimal training, reducing the need for human intervention. Future studies should expand the sample size and explore different approaches to improve its ability in research evaluation.

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