The role of AI in identifying or reconstituting archival aggregations of digital records and enriching metadata schemas

The overall goal of this study is to investigate the ability of AI to support creation (or recreation) of archival aggregations in order to address the issue of non-aggregated, unarranged, or de-contextualized records (both in the current and semi-current phases of their lifecycle). In many public administrations and private companies, documents are neither classified nor aggregated. In other cases, aggregations of documents are not properly created, which results in an uncontrolled number of documents that are unsorted, misplaced, and hard to find. In many cases metadata elements - necessary to ensure the reliability, trustworthiness, quality and sustainability of appraisal and acquisition - are missing. Despite progress on various technologies to support records management, current software products can only give limited help to carry out those activities. So, the research question this study aims to answer is: Can AI tools help to build or recreate archival aggregations and create metadata schemas for them?