Tracing the Past, Predicting the Future: A Systematic Review of <scp>AI</scp> in Archival Science

ABSTRACT The rapid expansion of content presents significant challenges in records management, notably in retention and disposition, appraisal, and organization. Our study highlights how integrating artificial intelligence (AI) into archival science can help address these issues. We begin with a thorough analysis of 45 papers published between 2011 and 2023 that met our predetermined criteria. All the articles were written in English; 40% of these were reviews, and the remaining 60% were original research articles. We investigated the key AI techniques and their applications in archives and records management functions. Our findings highlight key AI‐driven strategies that promise to streamline recordkeeping processes and improve data retrieval in the immediate future. This review outlines the current state of AI in archival science and records management and lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced interdisciplinary collaboration between AI experts and archival professionals.

Institutions

Publication Details

Journal
Proceedings of the Association for Information Science and Technology
Published
2025-10-01
DOI
https://doi.org/10.1002/pra2.1286
Citations
2
Primary Topic
Maritime Transport Emissions and Efficiency
Type
article
Field-Weighted Citation Impact
1.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tracing the Past, Predicting the Future: A Systematic Review of AI in Archival Science

2 citations
Proceedings of the Association for Information Science and Technology
Maritime Transport Emissions and Efficiency
1.60
article

Tracing the Past, Predicting the Future: A Systematic Review of AI in Archival Science

article en
2 citations

Abstract

ABSTRACT The rapid expansion of content presents significant challenges in records management, notably in retention and disposition, appraisal, and organization. Our study highlights how integrating artificial intelligence (AI) into archival science can help address these issues. We begin with a thorough analysis of 45 papers published between 2011 and 2023 that met our predetermined criteria. All the articles were written in English; 40% of these were reviews, and the remaining 60% were original research articles. We investigated the key AI techniques and their applications in archives and records management functions. Our findings highlight key AI‐driven strategies that promise to streamline recordkeeping processes and improve data retrieval in the immediate future. This review outlines the current state of AI in archival science and records management and lays the groundwork for integrating new techniques to transform archival practices. Our research emphasizes the necessity for enhanced interdisciplinary collaboration between AI experts and archival professionals.

Proceedings of the Association for Information Science and TechnologyVol. 62(1)
University of British Columbia (CA), San Jose State University (US)
Openalex Percentile: Top 15%
Maritime Transport Emissions and Efficiency
1.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Tracing the Past, Predicting the Future: A Systematic Review of <scp>AI</scp> in Archival Science · Proceedings of the Association for Information Science and Technology (2025) | TGRS Research Map | TGRS