Speaking with the Past: Constructing AI-Generated Historical Characters for Cultural Heritage and Learning

Recent advances in generative artificial intelligence (AI) have enabled the creation of AI-generated characters modeled after historical figures, offering new opportunities for reflective and interactive engagement in both cultural heritage and education. This study explores the development and evaluation of a large language model representation of Joseph Lister (1827–1912), a pioneer of antiseptic surgery, within a retrieval-augmented generation framework. The purpose was to examine the model’s accuracy, authenticity, and reliability, highlighting challenges, best practices, and ethical considerations. Drawing on primary and secondary sources, including Lister’s writings, the model was constructed using OpenAI’s GPT-4o and refined through iterative validation. Prompts were categorized by cognitive complexity, and responses were evaluated against historical materials. The findings revealed a strong fidelity to Lister’s voice, with appropriate tone, diction, and temporal limits. Moreover, the model demonstrated behavioral control, reflective depth, and consistency across the different prompts. However, minor lapses in temporal framing and occasional embellishments were noted. The findings suggest that, when developed with care, AI-generated characters can support ethically grounded, historically sensitive learning experiences. At the same time, this approach warrants continued scrutiny and underscores the need for further interdisciplinary research and responsible implementation.