On Artificial Intelligence as Assistive Technology in Archaeological Scholarship
Abstract
In archaeological scholarship, the challenge of interpreting large amounts of fragmented evidence can be daunting. This paper will examine how artificial intelligence can assist in this process, mainly through examining visual evidence and classifying material evidence. Drawing from Trevor Paglen's argument of machine vision and Matteo Pasquinelli's history of classification in machines, this paper develops an analysis of two archaeological case studies: archaeological site detection based on satellite imagery in Mesopotamia and deep-learning classification of Tusayan White Ware pottery. The findings of the studies demonstrate that artificial intelligence is capable of expanding preliminary surveys as well as applying typologies consistently. At the same time, they also highlight the importance of human input, coming to the conclusion that artificial intelligence's potential and principal contribution lies in simplifying the archaeological process and directing human attention towards evidence requiring further interpretation, establishing a framework for understanding artificial intelligence as an assistive technology.
References
- Paglen, T. (2026).How to see like a machine: Images after AI. London: Verso.
- Pasquinelli, M. (2023).The eye of the master: A social history of artificial intelligence. London: Verso.
- Davis, D. S. (2020). Defining what we study: The contribution of machine automation in archaeological research.Digital Applications in Archaeology and Cultural Heritage, 18, e00152.
- Orengo, H. A., Conesa, F. C., Garcia-Molsosa, A., Lobo, A., Green, A. S., Madella, M., & Petrie, C. A. (2020). Automated detection of archaeological mounds using machine-learning classification of multisensor and multitemporal satellite data.Proceedings of the National Academy of Sciences, 117(31), 18240–18250.
- Casini, L., Marchetti, N., Montanucci, A., Orrù, V., & Roccetti, M. (2023). A human–AI collaboration workflow for archaeological sites detection.Scientific Reports, 13, 8699.
- Pawlowicz, L. M., & Downum, C. E. (2021). Applications of deep learning to decorated ceramic typology and classification: A case study using Tusayan White Ware from Northeast Arizona.Journal of Archaeological Science, 130, 105375.
- Adams, R. McC. (1981).Heartland of cities: Surveys of ancient settlement and land use on the central floodplain of the Euphrates.Chicago:University of Chicago Press.
- University of Bologna. (n.d.). FloodPlains.OrientLab. Retrieved August 12, 2026, from https://floodplains.orientlab.net/