Abstract
This study explores the capability of academic staff assisted by the Turnitin Artificial Intelligence (AI) detection tool to identify the use of AI-generated content in university assessments. 22 different experimental submissions were produced using Open AI’s ChatGPT tool, with prompting techniques used to reduce the likelihood of AI detectors identifying AI-generated content. These submissions were marked by 15 academic staff members alongside genuine student submissions. Although the AI detection tool identified 91% of the experimental submissions as containing AI-generated content, only 54.8% of the content was identified as AI-generated, underscoring the challenges of detecting AI content when advanced prompting techniques are used. When academic staff members marked the experimental submissions, only 54.5% were reported to the academic misconduct process, emphasising the need for greater awareness of how the results of AI detectors may be interpreted. Similar performance in grades was obtained between student submissions and AI-generated content (AI mean grade: 52.3, Student mean grade: 54.4), showing the capabilities of AI tools in producing human-like responses in real-life assessment situations. Recommendations include adjusting the overall strategies for assessing university students in light of the availability of new Generative AI tools. This may include reducing the overall reliance on assessments where AI tools may be used to mimic human writing, or by using AI-inclusive assessments. Comprehensive training must be provided for both academic staff and students so that academic integrity may be preserved.
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Data Availability
The full commentaries left by academic staff members when marking the test submissions cannot be shared because of the confidentiality of the assessment results and associated comments. All other data generated or analysed during this study are included in this published article.
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The authors are very grateful for the support of the academic staff who participated in the study and the ongoing support of the university Examinations Office team who provided technical assistance throughout the project.
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Mike Perkins conceived and designed the study. Data collection and analysis were performed by the authors Mike Perkins, Darius Postma, James McGaughran, and Don Hickerson. The first draft of the manuscript was written by Mike Perkins, and all authors subsequently revised the manuscript. All authors have read and approved the final manuscript.
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Mike Perkins, Darius Postma, James McGaughran, and Don Hickerson are currently employed by the university where the study took place. Jasper Roe was previously employed at the same university. This study was not connected to or funded by Turnitin.
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The study was approved by the institution’s Human Ethics Committee before commencement, and all participants provided informed consent with the option to opt out of the study at any point in time.
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This study used Generative AI tools to produce draft text, and revise wording throughout the production of the manuscript. Multiple versions of ChatGPT over different time periods were used, with all versions using the underlying GPT-4 Large Language Model. The authors reviewed, edited, and take responsibility for all outputs of the tools used in this study.
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Perkins, M., Roe, J., Postma, D. et al. Detection of GPT-4 Generated Text in Higher Education: Combining Academic Judgement and Software to Identify Generative AI Tool Misuse. J Acad Ethics 22, 89–113 (2024). https://doi.org/10.1007/s10805-023-09492-6
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DOI: https://doi.org/10.1007/s10805-023-09492-6