The Role of AI in Identifying or Reconstituting Archival Aggregations of Records and Enriching Metadata Schemas

Artificial Intelligence (AI) is increasingly being integrated into archival science to address the challenges of managing, classifying, and reconstituting archival aggregations. This study explores the role of AI in identifying, organizing, and enriching metadata schemas for digital records. The research investigates AI's potential to support the creation or recreation of archival aggregations, particularly in cases where records lack contextualization or structured organization. Through an extensive review of AI-based archival management solutions and a survey of industry applications, the study assesses the effectiveness, limitations, and risks of AI tools in this domain. Findings indicate that while AI-driven classification and metadata enrichment show promise, full archival aggregation and contextual reconstruction still require significant human oversight. The research underscores the importance of collaboration between archivists and AI specialists to develop methodologies that balance automation with professional expertise. The study concludes that AI has the potential to enhance archival practices but is not yet a substitute for traditional archival processes, emphasizing the need for further refinement and evaluation of AI-driven approaches.