Artificial Intelligence and the Future of Historical Research: Opportunities, Biases, and Ethical Challenges - Dr. Malia Tui (Frontline Social Sciences and History Journal, 2026)
Artificial Intelligence (AI) is rapidly reshaping the landscape of historical research by enabling new methods of data interpretation, pattern recognition, and digital reconstruction of cultural and historical knowledge systems. This paper examines the transformative role of AI in historical inquiry, with a specific focus on opportunities for enhanced archival analysis, computational modeling of historical environments, and immersive reconstruction through augmented and virtual reality systems. At the same time, it critically evaluates the methodological, epistemological, and ethical challenges introduced by AI-driven historical interpretation. The study synthesizes insights from digital modeling frameworks, knowledge representation systems, and cultural heritage computing to construct a multidisciplinary perspective on AI-enabled historical research. Prior research demonstrates that modeling technologies such as Model Driven Architecture (MDA) and meta-modeling frameworks significantly improve the structuring and transformation of complex historical datasets (Hongxu Sun, 2012; Wile, 1997). Similarly, advances in virtual and augmented reality provide immersive pathways for historical reconstruction and education, allowing historians to simulate environments that are no longer physically accessible (Billinghurst, 2015; Kim et al., 2016). However, the integration of AI into historical analysis also introduces critical concerns regarding interpretive bias, algorithmic transparency, and epistemic reliability. As computational systems increasingly mediate historical narratives, the risk of reducing complex socio-cultural phenomena into overly deterministic models becomes significant. Furthermore, the reliance on structured modeling languages and automated extraction systems raises questions about data completeness and interpretive authority (Bork et al., 2018; Durisic et al., 2017). This paper argues that AI should be understood not as a replacement for traditional historiography but as an augmentative framework that enhances analytical depth while preserving interpretive plurality. It proposes a hybrid methodological approach combining computational modeling, archival theory, and critical historiography