## 2 Materials and Methods
### Tagging and Analysis  
There are several existing studies similar to ours, but they refer to other fields of science. Regardless, a brief overview of the methods used is in order. [PERSON] and colleagues [51], for example, provided prompts and content generated by ChatGPT and then discussed the quality of the content. [PERSON] [56] focused only on references and analysed factors such as the number of citations, the date of publication, and the journal in which the paper was published. [PERSON] and colleagues [57] posed questions and prompts to the model and refined them iteratively to produce a complete article, which was then reviewed by relevant experts for accuracy and coherence. [PERSON] and colleagues [59] tasked ChatGPT with writing a case report based on a draft report and evaluated its performance by comparing it to a case report written by human experts. Our approach is similar to that of [PERSON] and colleagues [57], but there are three significant differences. First, we did not use iterative refinement to ensure comparability between different AI chatbots. Second, we did not generate a complete paper. Third, our review was both qualitative and quantitative, not just qualitative. This was achieved by tagging the content. The aim was to provide what is, to our knowledge, the first quantitative comparison of different AI chatbots on the subject of scientific writing. The content generated by each of the tested AI chatbots was tagged for quantitative accuracy and qualitative precision. _Quantitative accuracy_ describes how accurate the content is in the opinion of the human experts (the authors). It was divided into five grades:  
* Correct: Factually correct and on par with the content created by human experts. * Inadequate: Factually correct but falls short of the content created by human experts. * Unverifiable: The statement cannot be verified or there is no expert consensus. * w/Errors: Mostly factually correct, but with important errors that change the meaning. * Incorrect: Factually incorrect. Quantitative accuracy is the measurement most commonly applied to AI-generated content, providing a quantifiable index of how trustworthy the tested AI chatbot is for the task at hand. It can be understood as similar to the grading of a student's work. The questions in this case study are on the level of a senior undergraduate student attending a class on Early Medieval archaeology. _Qualitative precision_ describes how \"good\" the content is in the opinion of a human expert. In other words, how it compares to a human-generated response in the context of scientific writing. It was divided into four grades:  
* Original scientific contribution. * Derivative scientific contribution. * Generic content not directly related to the question. * Incorrect: Factually incorrect, containing errors that change the meaning, or disputed (the last three classes of above quantitative tagging combined). Qualitative precision, as we have defined it, is specific to testing AI-generated content for the purpose of scientific writing and, to our knowledge, has not yet been used. The reason for the insufficient development of such indices is mainly that the current generation of AI chatbots is not expected to generate original scientific content. However, in the near future, AGI will be expected to produce it. The qualitative precision index was therefore developed with an eye to the future as a measure of how close the tested AI chatbots are to AGI. From an academic perspective, qualitative precision can be understood in a similar way to the peer review of a scientific paper. As with any peer review, e.g., [94], it is a combination of objective and subjective evaluation. It should be mentioned that in the humanities, an article usually consists of both derivative and original content. Typically, the introduction and method are predominantly derivative, while the results, discussion, and conclusion are predominantly original.