A comparison of Human, GPT-3.5, and GPT-4 Performance in a University-Level Coding Course

• URL: http://arxiv.org/abs/2403.16977v1
• Date: Mon, 25 Mar 2024 17:41:02 GMT
• Title: A comparison of Human, GPT-3.5, and GPT-4 Performance in a University-Level Coding Course
• Authors: Will Yeadon, Alex Peach, Craig P. Testrow,
• Abstract summary: This study evaluates the performance of ChatGPT variants, GPT-3.5 and GPT-4, against solely student work and a mixed category containing both student and GPT-4 contributions in university-level physics coding assignments using the Python language. Students averaged 91.9% (SE:0.4), surpassing the highest performing AI submission category, GPT-4 with prompt engineering, which scored 81.1% (SE:0.8) - a statistically significant difference (p = $2.482 times 10-10$) The blinded markers were tasked with guessing the authorship of the submissions on a four-point Likert scale from Definitely
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study evaluates the performance of ChatGPT variants, GPT-3.5 and GPT-4, both with and without prompt engineering, against solely student work and a mixed category containing both student and GPT-4 contributions in university-level physics coding assignments using the Python language. Comparing 50 student submissions to 50 AI-generated submissions across different categories, and marked blindly by three independent markers, we amassed $n = 300$ data points. Students averaged 91.9% (SE:0.4), surpassing the highest performing AI submission category, GPT-4 with prompt engineering, which scored 81.1% (SE:0.8) - a statistically significant difference (p = $2.482 \times 10^{-10}$). Prompt engineering significantly improved scores for both GPT-4 (p = $1.661 \times 10^{-4}$) and GPT-3.5 (p = $4.967 \times 10^{-9}$). Additionally, the blinded markers were tasked with guessing the authorship of the submissions on a four-point Likert scale from `Definitely AI' to `Definitely Human'. They accurately identified the authorship, with 92.1% of the work categorized as 'Definitely Human' being human-authored. Simplifying this to a binary `AI' or `Human' categorization resulted in an average accuracy rate of 85.3%. These findings suggest that while AI-generated work closely approaches the quality of university students' work, it often remains detectable by human evaluators.

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