The volume of academic and scientific publications grows rapidly, increasing the need for efficient mechanisms for accessing, obtaining and managing large collections of Open Access (OA) journal articles. For the purposes of an ongoing project requiring the analysis of thousands of OA Journal articles, a fast and reliable way to automatically download and rename PDF files was essential. To address this need, ChatGPT was employed to generate Python scripts from scratch, with the task deliberately assigned to a user with no Python programming experience, relying partially on his familiarity with HTML and CSS structures. Excluding one manually processed journal, which was used as a descriptive baseline, the study achieved a workflow-level success rate of 90.32% across the 31 AI-assisted journal workflows that were evaluated. Of these, 25 workflows were completed through fully functional downloader/renamer scripts, while three additional journals were processed through successful renaming workflows after automated downloading proved unsuccessful. Four MDPI journals were handled through a shared semi-automated workflow. The paper also presentsdescriptive observations from the documented workflow, indicating a gradual reduction in development time, prompts, and debugging iterations across later stages of the project, as the interaction process became more refined. Furthermore, within this feasibility case, the observed average operational time corresponded to approximately 15.8 s per file for the fully manual procedure, 13.8 s for the complete automated workflow corpus, and 10.8 s after excluding one highly time-consuming outlier case. Statistical analyses of the generated scripts, including imported modules, libraries, functions, constants, control structures, and total lines of code, are also presented. Overall, the study demonstrates the feasibility of AI-assisted scripting in one documented case involving a user without Python programming experience to accomplish tasks that were previously associated with programming expertise.
Rousidis et al. (Thu,) studied this question.