Between Impossible and Probable. Architectural Recognition Through Qualitative Evaluation of Artificial Intelligence Response

The paper investigates evaluation methodologies for AI-based virtual reconstruction of historical architecture, focusing on the uncertainty inherent in generative predictions. The research critically compares traditional analog reconstruction workflows with AI-driven reconstruction processes based on GAN networks, proposing a conceptual framework called the “Uncertainty Tree” to describe the decision-making chain involved in reconstruction. The study develops a qualitative evaluation methodology intended to complement quantitative pixel-based assessments by introducing prediction thresholds ranging from impossible to highly probable reconstructions. Through experiments on datasets of Greek Doric temples and Mudejar Romanesque churches, the research argues that specialized datasets and expert qualitative evaluation significantly improve the plausibility and reliability of AI-generated architectural reconstructions. The paper ultimately proposes a new epistemological framework for evaluating AI predictions in heritage reconstruction, emphasizing human supervision, dataset specificity, and iterative qualitative assessment.