The application of GPT-4 in grading design university students' assignment and providing feedback: An exploratory study

• URL: http://arxiv.org/abs/2409.17698v1
• Date: Thu, 26 Sep 2024 10:09:10 GMT
• Title: The application of GPT-4 in grading design university students' assignment and providing feedback: An exploratory study
• Authors: Qian Huang, Thijs Willems, King Wang Poon,
• Abstract summary: This study employs an iterative research approach in developing a Custom GPT. The inter-reliability between GPT and human raters reached a level that is generally accepted by educators. With adequate instructions, a Custom GPT gives consistent results which is a precondition for grading students.
• Score: 5.915297169078896
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study aims to investigate whether GPT-4 can effectively grade assignments for design university students and provide useful feedback. In design education, assignments do not have a single correct answer and often involve solving an open-ended design problem. This subjective nature of design projects often leads to grading problems,as grades can vary between different raters,for instance instructor from engineering background or architecture background. This study employs an iterative research approach in developing a Custom GPT with the aim of achieving more reliable results and testing whether it can provide design students with constructive feedback. The findings include: First,through several rounds of iterations the inter-reliability between GPT and human raters reached a level that is generally accepted by educators. This indicates that by providing accurate prompts to GPT,and continuously iterating to build a Custom GPT, it can be used to effectively grade students' design assignments, serving as a reliable complement to human raters. Second, the intra-reliability of GPT's scoring at different times is between 0.65 and 0.78. This indicates that, with adequate instructions, a Custom GPT gives consistent results which is a precondition for grading students. As consistency and comparability are the two main rules to ensure the reliability of educational assessment, this study has looked at whether a Custom GPT can be developed that adheres to these two rules. We finish the paper by testing whether Custom GPT can provide students with useful feedback and reflecting on how educators can develop and iterate a Custom GPT to serve as a complementary rater.

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