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.

The scientific community considers readership analysis of academic artifacts to be a significant endeavor. The reference manager’s readership count is a momentous indication for early research evaluation. In response, this study demonstrates the characteristics of Mendeley readership for EPS articles from twelve narrow disciplines and compares them with citations. The bibliographic and citation data have been collected from Scopus and the corresponding readers’ data from Mendeley. The Spearman correlation was performed among citations and readers for all unique articles for all investigated disciplines. Further, we also looked at the relationships between articles with non-zero readers, as well as articles satisfied by percentile ranking of the top 75 per cent, 50 per cent, and 25 per cen treaders. The result indicates large correlations among citations and readers (avg. 0.669) for all investigated disciplines. If we analysed only non-zero readers, as well as a percentile ranking of articles, the correlation results show a decreasing trend. Around 98.57 per cent of articles have at least one reader in Mendeley and AS (97.53 %) discipline has registered the highest one. The CES discipline had registered the largest MRS of 32.25 and MCS of 12.75. Most of the readers come from post-doctoral students and Ph.D. students. The correlation results indicate that the readership statistics should be used as an impact indicator for EPS discipline.