Testing the Feasibility of Schema.org Metadata Refinement Through the Use of a Large Language Model.

This article describes an experiment to use the ChatGPT Large Language Model as a tool to refine Schema.org metadata. ChatGPT was asked to give suggestions to improve a preexisting package of Schema.org structured metadata in the NMSU Library homepage for search engine optimization. A package of reformatted metadata based on ChatGPT's recommendations was used to replace the preexisting metadata for seven weeks and relevant web stats are compared to an equivalent seven-week period from the preceding semester. This article discusses ChatGPT's recommendations in some depth and examines the outcomes from a theoretical perspective. Implications of the experiment are outlined along with future areas for research. [ABSTRACT FROM AUTHOR]