A comparison of Human, GPT-3.5, and GPT-4 Performance in a University-Level Coding Course
This research addresses the problem of evaluating AI capabilities in educational coding tasks for educators and AI developers, but it is incremental as it applies existing methods to new data.
This study compared the performance of GPT-3.5 and GPT-4 with and without prompt engineering against student work in university-level physics coding assignments, finding that students averaged 91.9% while the best AI (GPT-4 with prompt engineering) scored 81.1%, a statistically significant difference, and human markers could detect AI authorship with 85.3% accuracy.
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.