Towards Archival Engagement With AI: An exploratory study using historical photographs

As archives and cultural heritage institutions experiment with immersive and semi-immersive technologies, generative artificial intelligence is increasingly proposed as a means of expanding access to archival materials. This exploratory case study evaluates three commercial artificial intelligence tools: Skybox, LTX Studio, and DeeVid by applying them to thirty-three historical photographs from the Arizona Historical Society’s Tucson 250+: Where We Live, What We Do, and Who We Are digital exhibition. Using a rubric-based evaluation framework, the study assesses fidelity, coherence, authenticity, engagement, and representational accuracy. The findings reveal systematic differences across platforms: Skybox frequently produces historically inaccurate outputs in scenes involving people and identity markers; LTX Studio demonstrates greater visual consistency but introduces subtle representational distortions; and DeeVid generates stable but limited transformations. The results indicate that generative tools currently require substantial human oversight to align with archival standards and ethical responsibilities. Also, the results point to the importance of conceptually informed and logic-based approaches in the development of artificial intelligence tools for archival applications.