A comparison of Human, GPT-35, and GPT-4 Performance in a University-Level Coding Course
<|reference_start|>A comparison of Human, GPT-35, and GPT-4 Performance in a University-Level Coding Course: 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.<|reference_end|>
@article{yeadon2024a,
    title={A comparison of Human, GPT-3.5, and GPT-4 Performance in a
  University-Level Coding Course},
    author={Will Yeadon, Alex Peach, Craig P. Testrow},
    journal={arXiv preprint arXiv:2403.16977},
    year={2024},
    doi={10.1038/s41598-024-73634-y},
    archivePrefix={arXiv},
    eprint={2403.16977},
    primaryClass={cs.CL}
}