AI-Generated Content Detectors: Boon or Bane for Scientific Writing
Namrata Ladha, Kaushal Yadav, Priya Rathore
Abstract
Objectives: The purpose of the study is to help publishers identify AI-generated text in scientific research, academic works, and assignments as a critical step toward the regulation and promotion of the ethical usage of AI in academia. Method: Recently developed literature on Generative AI suggests that human reviewers may fail to distinguish between human and AI-generated articles. Therefore, the present study evaluates AI-powered software as a potential solution for AI-generated content detection. We performed an experiment to see whether AI detector tools are capable of identifying and distinguishing between human- and AI-generated texts. To determine the accuracy of the AI-detector, we created and submitted four research articles to AI-detector tools for a pre- and post- manipulation test. Findings: The study shows that it is quite impossible for any AI detector to identify all AI-generated content accurately, thus, human-AI collaboration strategies can be employed to achieve the maximum accuracy. This paper demonstrates in a novel manner how AI detector tools can be manipulated to provide false results. Novelty: To the best of the authors’ knowledge, this paper is the first to acknowledge growing AI-literacy among students and scholars. This study edifies academia by providing scientifically verified human-AI collaboration strategies to capitalize on these tools and thwart academic misconduct. Keywords: Artificial Intelligence, Chatbot, ChatGPT, Generative AI, Scientific Research