Training in Computational Archival Science: Do CAS Educational Frameworks meet Professional Expectations?

Abstract

This paper explores the evolving landscape of training for archival professionals in the context of big data and emerging technologies. By comparing two educational frameworks—the CAS framework, developed from computational thinking research and CAS research papers, and the InterPARES framework, based on empirical studies with archivists working with AI/ML, we identify areas of alignment and divergence. While both frameworks share significant concordance, suggesting a growing consensus on integrating computing into archival work, key differences in their approaches (learning outcomes vs. competencies) and focus areas (such as work practices, systems thinking, and cybersecurity) highlight the need for further discourse among archival scholars, educators, and practitioners. These distinctions must be addressed before formalizing CAS educational frameworks. This paper also initiates efforts to integrate emerging technological competencies by bridging the CAS and InterPARES frameworks, emphasizing the value of complementary perspectives from both professional practice and academic research. We argue that such integration is essential for developing robust competency frameworks in archival education, particularly within higher education's professional programs.

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