Design and Evaluation of a Historical Conversational Agent Embodying Carlo Pisacane

This paper presents a historical conversational agent embodying Carlo Pisacane. Conversational agents based on Large Language Models offer new opportunities for education and cultural heritage. However, they suffer from inaccuracies and temporal inconsistencies. To address this, we adopt a Retrieval-Augmented Generation (RAG) approach that grounds responses in a historical documentary corpus. We propose a framework combining document grounding, temporal constraints, and persona modeling. Multiple language models were tested to evaluate narrative coherence, historical accuracy, and mitigation of hallucinations. Results show that medium-sized models provide the best trade-off between generative quality and factual fidelity. A user study (n=52) reports high usability, engagement, and educational value. These findings demonstrate the potential of RAG-based historical agents to enhance learning and cultural engagement.