Scholarly Communication and Machine-Generated Text: Is it Finally AI vs AI in Plagiarism Detection?


  • Assistant Librarian, DSMS College, Durgapur – 713206, Durgapur, West Bengal
  • Research Scholar, Banaras Hindu University, Varanasi – 221002, Uttar Pradesh



AI (Artificial Intelligence), GPT (Generative Pre-Training Transformer), Machine Learning, ChatGPT, Natural Language Processing (NLP), OpenAI, Plagiarism


This study utilizes GPT (Generative Pre-Trained Transformer) language model-based AI writing tools to create a set of 80 academic writing samples based on the eight themes of the experiential sessions of the LTC 2023. These samples, each between 2000 and 2500 words long, are then analyzed using both conventional plagiarism detection tools and selected AI detection tools. The study finds that traditional syntactic similarity-based anti-plagiarism tools struggle to detect AI-generated text due to the differences in syntax and structure between machine-generated and human-written text. However, the researchers discovered that AI detector tools can be used to catch AI-generated content based on specific characteristics that are typical of machine-generated text. The paper concludes by posing the question of whether we are entering an era in which AI detectors will be used to prevent AI-generated content from entering the scholarly communication process. This research sheds light on the challenges associated with AI-generated content in the academic research literature and offers a potential solution for detecting and preventing plagiarism in this context.


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How to Cite

Santra, P. P., & Majhi, D. (2023). Scholarly Communication and Machine-Generated Text: Is it Finally AI vs AI in Plagiarism Detection?. Journal of Information and Knowledge, 60(3), 175–183.