Recent advances in generative Artificial Intelligence (AI) have rapidly transformed research and practice across the Cultural Heritage domain. While several studies have investigated AI applications in documentation, analysis and dissemination, a focused and critical assessment of generative AI within 3D virtual reconstruction workflows is still lacking. This paper presents a systematic review of the literature addressing the use of generative AI in 3D heritage virtual reconstructions, with particular attention to methodological implications, scientific reliability and ethical challenges. A large-scale bibliographic analysis covering publications from 2015 to 2024 was conducted using OpenAlex, complemented by targeted manual searches. From an initial corpus of over 8700 papers on 3D heritage reconstruction, only 13 directly addressed generative AI-driven reconstruction processes. The analysis highlights a significant gap between the rapid technological development of AI-based tools and their cautious, often problematic, adoption in virtual reconstruction practices. Results reveal recurring issues related to terminological ambiguity, opacity of reconstruction processes, evaluation metrics focused on visual plausibility rather than scientific transparency and the risk of interpretative bias. The paper argues that current AI-driven approaches tend to privilege speed and aesthetic outcomes over heuristic, source-based reconstruction workflows. Finally, future research directions are discussed, emphasizing the potential role of AI as an evaluative and analytical support tool rather than a fully autonomous reconstruction agent, in alignment with established charters and principles of virtual archaeology.
Lombardi et al. (Tue,) studied this question.