Evaluating the authenticity of ChatGPT responses: a study on text-matching capabilities

Abstract

Academic plagiarism is a pressing concern in educational institutions. With the emergence of artificial intelligence (AI) chatbots, like ChatGPT, potential risks related to cheating and plagiarism have increased. This study aims to investigate the authenticity capabilities of ChatGPT models 3.5 and 4 in generating novel, coherent, and accurate responses that evade detection by text-matching software. The repeatability and reproducibility of both models were analyzed, showing that the generation of responses remains consistent. However, a two-sample t-test revealed insufficient evidence to support a statistically significant difference between the text-matching percentages of both models. Several strategies are proposed to address the challenges posed by AI integration in academic contexts; one probable solution is to promote self-transcendent ideals by implementing honor codes. It is also necessary to consider the restricted knowledge base of AI language models like GPT and address any inaccuracies in generated references. Additionally, designing assignments that extract data from imaged sources and integrating oral discussions into the evaluation process can mitigate the challenges posed by AI integration. However, educators should carefully consider the practical constraints and explore alternative assessment methods to prevent academic misconduct while reaping the benefits of these strategies.