Publication Details
Abstract
This research aims to explore the linguistic and technical dimensions of Generative AI and its pivotal impact on integrity standards in scientific research. Amidst the rapid escalation of Large Language Models (LLMs) in simulating human style the issue of research identity erosion emerges alongside novel patterns of "semantic plagiarism" that surpass traditional matching algorithms. The study employs a descriptive-analytical and comparative approach to deconstruct the semantic gap between stereotypical machine text and humanized text. The findings indicate that AI practices a form of authenticity forgery through deep paraphrasing, necessitating the adoption of a humanization protocol to enhance the researcher's individual signature. The study recommends developing hybrid detection systems that integrate statistical analysis with linguistic context, emphasizing the importance of ethical disclosure regarding the role of technology in academic achievements to preserve the sovereignty of the human mind.