Game of Tones: Faculty detection of GPT-4 generated content in university assessments

TL;DR

This study assesses how well faculty can detect GPT-4 generated content in university assessments, revealing limitations of current AI detection tools and suggesting strategies to maintain academic integrity.

Contribution

It provides empirical evidence on the effectiveness and limitations of AI detection tools in academic settings and highlights the need for improved detection methods and assessment strategies.

Findings

Detection tool identified 91% of AI submissions but only 54.8% of total AI content

Faculty reported 54.5% of AI-generated submissions as misconduct

AI-generated content scored similarly to genuine submissions in assessments

Abstract

This study explores the robustness of university assessments against the use of Open AI's Generative Pre-Trained Transformer 4 (GPT-4) generated content and evaluates the ability of academic staff to detect its use when supported by the Turnitin Artificial Intelligence (AI) detection tool. The research involved twenty-two GPT-4 generated submissions being created and included in the assessment process to be marked by fifteen different faculty members. The study reveals that although the detection tool identified 91% of the experimental submissions as containing some AI-generated content, the total detected content was only 54.8%. This suggests that the use of adversarial techniques regarding prompt engineering is an effective method in evading AI detection tools and highlights that improvements to AI detection software are needed. Using the Turnitin AI detect tool, faculty reported 54.5% of the experimental submissions to the academic misconduct process, suggesting the need for increased awareness and training into these tools. Genuine submissions received a mean score of 54.4, whereas AI-generated content scored 52.3, indicating the comparable performance of GPT-4 in real-life situations. Recommendations include adjusting assessment strategies to make them more resistant to the use of AI tools, using AI-inclusive assessment where possible, and providing comprehensive training programs for faculty and students. This research contributes to understanding the relationship between AI-generated content and academic assessment, urging further investigation to preserve academic integrity.