## 4 Discussion: Data to Knowledge  
The results of our analysis show that ChatGPT, while currently the most advanced AI chatbot, is not capable of producing original scientific content. This is a zero-shot learning problem, but it is also much more than that. Namely, LLM-based AI chatbots are not designed to generate content in the same way that human researchers produce new knowledge.  
A typical process for producing original scientific contributions, which was also used in our case study, is an intricate process that is perhaps best explained using [PERSON]'s Data-Information-Knowledge-Wisdom (DIKW) hierarchy. According to this hierarchy, data are the raw input, simple facts without context; information is data that has been given context and is meaningful in some way; knowledge is the understanding and interpretation of that information gained through experience or learning; and wisdom, the highest level of the pyramid, is the ability to apply knowledge judiciously and appropriately in different contexts, understand the broader implications, and make insightful decisions based on accumulated knowledge and experience [111].  
In our case study, pertinent to Q2, the data were the documentation of the excavations of 1106 archaeological sites. The information was summaries of the excavation reports and scientific publications on these 1106 sites curated in the structured database Zbiva, available on the public web since 2016 [112]. Knowledge was the scholarly articles discussing and/or analysing the migration of the Alpine Slavs. Wisdom is what happens (rarely) after the publication of scientific articles and after the generation of AI chatbot content and does not concern us here.  
Human researchers approached Q2 by first obtaining and refining the data, which was followed by analysing the information using appropriate software, formulating knowledge based on the results, and finally disseminating the knowledge in the form of an original scientific article.  
In the real world, archaeological practice (and most humanities research) is messy, and therefore, this process is fuzzy and recursive. It takes the form of a hermeneutic spiral [113], which, from the perspective of computational algorithms, are loops between data, information, and knowledge. These loops involve solving computationally irreducible processes that cannot be handled by LLM-based AI chatbots alone (Figure 1: I).  
In other words, generating original scientific content requires not only access to curated data/information, but also the ability to analyse it.  
LLMs, on the other hand, are pre-trained on existing knowledge (texts, books) and are only able to recombine it in new permutations. Occasionally, this can lead to modest original scientific content. For instance, AI chatbots could be used to summarize what a historical text verbatim says about a certain subject, but not to interpret it. Therefore, this is a limited and finite avenue to original scientific contributions. Regardless of the future improvement of LLMs, LLM-based AI chatbots will not be able to replicate the workflow of human researchers, as they are only trained on the existing knowledge.  
A purpose-built LLM-powered software, on the other hand, could handle such a workflow: searching for data/information, performing relevant analysis, generating textual and graphical information, and summarising it into new knowledge (Figure 1: K-M; G, J). Such LLM-powered software would have several qualities of an AGI and is in fact already feasible with existing technology, for example by combining Prometheus, a relevant cloud-based software connected through a ChatGPT API, and ChatGPT-4.  
In a nutshell, LLM-based AI chatbots are not and probably will never be able to generate new knowledge from data in the same way as human researchers (in the humanities), but appropriate LLM-powered software might be.