A Study on Human-AI Collaborative Workflows for Academic Resource Data Generation

Abstract

　The generation of academic resource data from scholarly materials . such as historical documents and folk artifacts . encompasses multiple processes, including metadata creation, image capture, data organization, and verification for publication, all of which require diverse specialized expertise. However, a shortage of academic professionals due to population decline and growing societal demand for rapid data publication have made it increasingly urgent to streamline and optimize these workflows. The authors have extensive experience in surveying regional and scholarly materials, generating data, and publishing digital archives. In response to the rapid advancement of generative AI technologies, they have been working to revise and optimize academic resource data generation workflows through the application of these new tools. This report presents an overview and key challenges of a newly proposed human-machine collaborative workflow, in which humans and AI systems work in concert to improve efficiency throughout the data generation and publication process.