Evaluating AI and Human Authorship Quality in Academic Writing through Physics Essays

TL;DR

This study compares AI-generated and human physics essays, finding no significant quality difference and highlighting the challenge of distinguishing AI content through human judgment, while evaluating AI detection tools.

Contribution

It provides empirical evidence that AI and human essays are indistinguishable in quality and assesses the effectiveness of AI authorship detection tools.

Findings

No significant score difference between AI and human essays

Detection tools vary in accuracy, with ZeroGPT achieving 98% accuracy

Humans struggle to reliably identify AI-generated essays

Abstract

This study evaluates $n = 300$ short-form physics essay submissions, equally divided between student work submitted before the introduction of ChatGPT and those generated by OpenAI's GPT-4. In blinded evaluations conducted by five independent markers who were unaware of the origin of the essays, we observed no statistically significant differences in scores between essays authored by humans and those produced by AI (p-value $= 0.107$, $\alpha$ = 0.05). Additionally, when the markers subsequently attempted to identify the authorship of the essays on a 4-point Likert scale - from `Definitely AI' to `Definitely Human' - their performance was only marginally better than random chance. This outcome not only underscores the convergence of AI and human authorship quality but also highlights the difficulty of discerning AI-generated content solely through human judgment. Furthermore, the effectiveness of five commercially available software tools for identifying essay authorship was evaluated. Among these, ZeroGPT was the most accurate, achieving a 98% accuracy rate and a precision score of 1.0 when its classifications were reduced to binary outcomes. This result is a source of potential optimism for maintaining assessment integrity. Finally, we propose that texts with $\leq 50\%$ AI-generated content should be considered the upper limit for classification as human-authored, a boundary inclusive of a future with ubiquitous AI assistance whilst also respecting human-authorship.