Intelligent systems for digital curation and archival discovery

This article examines the paradigm-shifting role of intelligent systems in redefining digital curation and archival discovery. Confronted by the overwhelming scale and complexity of born-digital records, traditional archival methods are augmented through a collaborative partnership with artificial intelligence. The analysis explores how machine learning transforms core archival functions: computational appraisal triages vast collections using pattern recognition; AI-driven arrangement reconstructs intellectual order from digital chaos; and predictive preservation proactively safeguards assets. The most profound transformation occurs in discovery, where semantic search, cross-modal analysis, and network visualization enable dynamic, hermeneutic exploration of collections, moving users from targeted query to contextual interpretation. The paper critically addresses the necessary ethical and professional framework for this integration, arguing that the archivist’s role evolves into that of a “cyborg curator” who applies expert judgment, contextual wisdom, and ethical stewardship to direct and interrogate algorithmic tools. Ultimately, intelligent systems are positioned not as replacements for human expertise but as essential collaborators, transforming archives from static repositories into proactive, intelligently responsive knowledge environments that can preserve and illuminate the digital patrimony with unprecedented depth and resilience.