We are pleased to announce the Using AI for Humanities Research Software guide, which offers a practical starting point for humanities researchers, students, Research Software Engineers and instructors who want to understand how AI tools can support research software.
Humanities research increasingly uses digital collections, large textual datasets and computational tools. AI techniques such as Natural Language Processing, Machine Learning and Computer Vision are becoming more common in software used for text analysis, archival processing, manuscript analysis and research workflows.
The guide introduces practical applications of AI in humanities research software, including text mining, manuscript analysis, Optical Character Recognition, handwriting recognition, computer vision and the use of generative AI to support coding, metadata and workflow tasks.
It also highlights the need for critical interpretation, transparency and responsible practice. AI-powered tools can support research, but they can also introduce errors, bias and uncertainty. Researchers need to understand how these tools are being used, check outputs carefully and document their use of AI in the research process.
After reading the guide, readers should be able to recognise how AI can be used to create and deploy software for humanities research, identify key applications for analysing textual, visual and cultural datasets, and apply responsible practices when using AI-powered research tools.