Artificial Intelligence in Archival Science: Enhancing Records Preservation, Retrieval Accuracy, and Knowledge Accessibility in the Digital Era

Endang Fatmawati; Shamila Mohamed  Shuhidan; Samsul Farid  Samsuddin; Jazimatul  Husna; Yayuk Endang  Irawati; Minan Faiz Fausta  Rafa

doi:10.54783/endlessjournal.v9i2.381

Authors

Endang Fatmawati Diponegoro University, Semarang, Indonesia
Shamila Mohamed Shuhidan Universiti Teknologi Mara, Malaysia
Samsul Farid Samsuddin Universiti Malaya, Malaysia
Jazimatul Husna Diponegoro University, Semarang, Indonesia
Yayuk Endang Irawati Diponegoro University, Semarang, Indonesia
Minan Faiz Fausta Rafa Diponegoro University, Semarang, Indonesia

DOI:

https://doi.org/10.54783/endlessjournal.v9i2.381

Abstract

This study examines the transformative role of artificial intelligence (AI) in archival science, with a particular focus on enhancing records preservation, retrieval accuracy, and knowledge accessibility in the digital era. The increasing volume and complexity of digital records have posed significant challenges for traditional archival practices, necessitating the integration of advanced technological solutions. In response, AI has emerged as a promising approach to automate archival processes and improve the efficiency of information management systems. This study adopts a literature review methodology by systematically analyzing relevant scholarly publications to synthesize current knowledge on AI applications in archival contexts. The review draws upon peer-reviewed articles indexed in reputable databases to ensure the credibility and relevance of the findings. The results indicate that AI technologies, including machine learning, natural language processing, and computer vision, significantly contribute to improving digital preservation strategies and minimizing data degradation risks. AI-driven retrieval systems enhance the precision and speed of information access through intelligent indexing and semantic search capabilities. The study also finds that AI facilitates broader knowledge accessibility by enabling user-centered interfaces and adaptive information systems. Despite these advancements, several challenges persist, including ethical concerns, data bias, and the need for standardized implementation frameworks. This study contributes to the growing body of knowledge by providing a comprehensive synthesis of AI applications in archival science and offering insights for future research and practical implementation.

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