Admissions in the age of AI: detecting AI-generated application materials in higher education

Yijun Zhao; Alexander Borelli; Fernando Martinez; Haoran Xue; Gary M Weiss

doi:10.1038/s41598-024-77847-z

Admissions in the age of AI: detecting AI-generated application materials in higher education

Sci Rep. 2024 Nov 2;14(1):26411. doi: 10.1038/s41598-024-77847-z.

Authors

Yijun Zhao¹, Alexander Borelli², Fernando Martinez², Haoran Xue², Gary M Weiss³

Affiliations

¹ Computer and Information Sciences Department, Fordham University, New York, NY, 10023, USA. yzhao11@fordham.edu.
² Computer and Information Sciences Department, Fordham University, New York, NY, 10023, USA.
³ Computer and Information Sciences Department, Fordham University, New York, NY, 10023, USA. gaweiss@fordham.edu.

Abstract

Recent advances in Artificial Intelligence (AI), such as the development of large language models like ChatGPT, have blurred the boundaries between human and AI-generated text. This has led to a pressing need for tools that can determine whether text has been created or revised using AI. A general and universally effective detection model would be extremely useful, but appears to be beyond the reach of current technology and detection methods. The research described in this study adopts a domain and task specific approach and shows that specialized detection models can attain high accuracy. The study focuses on the higher education graduate admissions process, with the specific goal of identifying AI-generated and AI-revised Letters of Recommendation (LORs) and Statements of Intent (SOIs). Detecting such application materials is essential to ensure that applicants are evaluated on their true merits and abilities, and to foster an equitable and trustworthy admissions process. Our research is based on 3755 LORs and 1973 SOIs extracted from the application records of Fordham University's Master's programs in Computer Science and Data Science. To facilitate the construction and evaluation of detection models, we generated AI counterparts for each LOR and SOI using the GPT-3.5 Turbo API. The prompts for AI-generation text were derived from the admission data of the respective applicants, and the AI-revised LORs and SOIs were generated directly from the human-authored versions. We also utilize an open-access GPT-wiki-intro dataset to further validate our hypothesis regarding the feasibility of constructing domain-specific AI content detectors. Our experiments yield promising results in developing classifiers tailored to a specific domain when provided with sufficient training samples. Additionally, we present a comparative analysis of the word frequency and statistical characteristics of the text, which provides convincing evidence that ChatGPT employs distinctive vocabulary and paragraph structure compared to human-authored text. The code for this study is available on GitHub, and the models can be executed on user-provided data via an interactive web interface.