Research Article

Artificial Intelligence–Empowered Cultural Heritage Experience Design: A Case Study of Pompeii

Meiqi WangKyiv Institute at Qilu University of Technologies*

* Corresponding author: [email protected]

Abstract

This study looks at how artificial intelligence works with cultural heritage experience design. It takes the ancient city of Pompeii as a study example. The results show that AI technologies are very important for rebuilding old scenes and giving personal guided tours. But AI can work well only when heritage information is digitized fully and correctly. It also needs to follow basic design rules. These rules include putting protection first, keeping the experience real, and using proper technology. The main new point of this study is building a complete framework. This framework includes "heritage information–AI technology–user experience". The study also makes smart experience plans based on real situations. These plans link the past and the present in a lively way. There are still some problems now. For example, technology costs a lot, data is not complete enough, and privacy is hard to protect. But future studies can make immersive experiences better by using multimodal interaction. These methods can also be used in more cultural heritage places. This will help cultural heritage become active again with smart technology and be passed on well for a long time.

Keywords: cultural heritage; artificial intelligence; digital experience; Pompeii
Published: April 7, 2026
DOI: 10.54254/2753-7064/2026.HT32612
Volume: CHR Vol.106
pp. 1-11
Download PDF

References

  1. Song, J. H., & Wang, M. Y. (2015). Current status and issues of digital protection of China's intangible cultural heritage. Cultural Heritage, (6), 1–9, 157.
  2. Yıldırım, S., & Köse, D. (2023). Artificial intelligence in cultural heritage: A systematic literature review. Heritage, 6(8), 5966–5986. https: //doi.org/10.3390/heritage6080307
  3. Mazzaglia, A. (2021). The information system of Pompeii sustainable preservation project: A tool for the collection, management and sharing of knowledge useful for conservation and renovation of archaeological monuments. Environmental Sciences Proceedings, 10(1), 14. https: //doi.org/10.3390/environsciproc2021010014
  4. Bright, A., Kay, J., Ler, D., et al. (2009). MyMuseum: A participatory, personalized and adaptive museum guide. In Smart Environments and their Applications to Cultural Heritage (pp. 1–10). EPOCH Network.
  5. Magnenat-Thalmann, N., et al. (2023). Recreating daily life in Pompeii with mixed reality and AI-driven virtual humans. Proceedings of VRIC 2023. https: //doi.org/10.1145/358801.358817
  6. Li, Q., Zhang, J., Wang, T., et al. (2024). Geospatial intelligence for emergency rescue: Concept, generation techniques, and application practices. Journal of Wuhan University (Information Science Edition), 49(3), 415–424. https: //doi.org/10.13203/j.whugis20240098
  7. Xu, D. (2021). Exploration on the interpretation and utilization models of cultural heritage. In Proceedings of the Annual Conference of China Urban Planning Society. Beijing, China: Tsinghua University.
  8. Wang, J., & Sun, Y. L. (2025). AI-enabled digital cultural heritage: Theoretical evolution and tourism applications. Science (Shanghai), (4).
  9. Adamopoulos, E., & Rinaudo, F. (2021). Automating degradation mapping of ancient stelae by dual-band imaging and machine learning-based classification. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, VIII-M-1-2021, 9–16. https: //doi.org/10.5194/isprs-annals-VIII-M-1-2021-9-2021
  10. Hu, Y. N., Li, G. Y., Han, X. D., Jian, L., & Zhang, G. H. (2022). Virtual restoration of mural images based on dual-discriminator generative adversarial networks. Foreign Electronic Measurement Technology, 41(6), 14–19. https: //doi.org/10.19652/j.cnki.femt.2203744