Objective: History research institutes play a significant role in preserving, organizing, and disseminating historical and documentary knowledge. However, the diversity of historical resources, fragmentation of information, and dependence on researchers' tacit knowledge create challenges for effective knowledge management. Artificial intelligence offers opportunities to address these challenges through intelligent knowledge organization, retrieval, and analysis. This study aimed to identify and explain the dimensions and indicators of AI-oriented knowledge management in history research institutes.
Methodology: This applied qualitative study employed the meta-synthesis method to analyze domestic and international research on knowledge management and artificial intelligence. Iranian studies published between 2016 and 2025 and international studies published between 2010 and 2025 were considered. Initially, 137 studies were identified, of which 125 remained after preliminary screening. Following quality assessment using the Critical Appraisal Skills Programme (CASP), 72 articles were selected for final analysis. Relevant concepts were extracted through open coding and organized into subcategories, main categories, and overarching dimensions. Subsequently, experts in knowledge management and artificial intelligence were interviewed to examine the relevance, comprehensiveness, and conceptual consistency of the identified categories. Their suggestions were incorporated into the final structure.
Findings: The findings identified seven dimensions of AI-oriented knowledge management: knowledge leadership and governance, knowledge life cycle, knowledge assets and representation, human capital and knowledge-oriented culture, knowledge technology and infrastructure, intelligent knowledge management, and knowledge evaluation and value creation. These dimensions highlight the interdependence of organizational governance, knowledge processes, human competencies, technological capabilities, intelligent knowledge applications, and continuous evaluation in managing historical knowledge resources.
Conclusion: Effective AI-oriented knowledge management in history research institutes requires integrating technological capabilities with organizational governance, human expertise, and knowledge-sharing practices. The identified dimensions and indicators provide a conceptual foundation for improving the preservation, organization, retrieval, and utilization of historical knowledge. They may also guide institutional planning, support informed decision-making, and facilitate the responsible adoption of artificial intelligence while maintaining the authenticity and reliability of historical resources.