Keynote : Teaching Computational Archival Science

Computational Archival Science (CAS) [see: https://ai-collaboratory.net/cas], was designated in 2016 against the background of the transformative effects of computing technology on archival practice and theory, by a group of technologists and archivists, including from the US, Canada, and the UK. A Foundational paper was drafted that year and later published in 2018 (“Archival records and training in the Age of Big Data”). The authors made a case for a new transdiscipline, which they defined as:

“A transdisciplinary field grounded in archival, information, and computational science that is concerned with the application of computational methods and resources, design patterns, sociotechnical constructs, and human-technology interaction, to large-scale (big data) records/archives processing, analysis, storage, long-term preservation, and access problems, with the aim of improving and optimizing efficiency, authenticity, truthfulness, provenance, productivity, computation, information structure and design, precision, and human technology interaction in support of acquisition, appraisal, arrangement and description, preservation, communication, transmission, analysis, and access decisions.”

While CAS is still an emerging field, it has the potential to bridge traditional and new topics in iSchools. One of the most important lessons learned has been the significance of integrating computational thinking (CT) concepts into archival science, which parallels the case for the inclusion of CT in mathematics and K-12 science classrooms. Our approach emphasizes developing CAS with a focus on community and social justice

This talk explores the context of CAS, key pedagogical developments that have emerged, and future directions in metadata including advances in Generative AI and Large Language Models.