Adaptive learning models for efficient and standardized archival processes

Abstract

Integrating adaptive learning model development into archival processing presents an exciting opportunity to tackle challenges such as labor-intensive manual tasks, lack of uniformity, and ineffective feedback integration. This pilot project explores the practical application of adaptive learning models and natural language processing (NLP) techniques to streamline archival workflows, improve data precision, and consistently implement improved best practices and standards. This research presents an overview of the methodologies and frameworks used, presenting an improved model for automated archival processing. In addition, the research investigates the wider impact of large-scale digital projects on the future of archival science. The capability to quickly process extensive amounts of both typewritten and handwritten text within minutes that traditionally have taken hours, days, or even months. This process is a major breakthrough in the field of archival science. This new development not only makes it easier to handle collections but also changes how we standardize and manage archives, making the process more efficient and accessible for everyone. We can identify patterns, entities, subjects, and policies effectively by accelerating text analysis, interpretation, and refining control terminology. This, in turn, significantly enhances our ability to share information, amplifying the value and impact of the technology. Through rigorous and extensively tested modeling, this system enhances internal data linkage and establishes robust external connections, significantly amplifying our capacity to manage and utilize archival information.