## 1 Introduction & Background  
In recent years, extended reality (XR) technology has rapidly evolved from a mediatic fantasy to multimodal interaction platforms that support a vast variety of content and are expected to expand even more [1, 2]. Through its generous affordances, XR is transforming the way we create [3], understand [4] and learn [5]. Its use in education, in particular, has been rigorously researched, such as in [6], indicating improved student engagement, retention, and interest. Traditionally linear subjects, such as history, can be transformed into immersive experiences, focusing on presence and interactivity [7].  
There have been several efforts to recreate historical spaces and events in more interactive ways. The authors in [8] designed an elaborate educational VR experience that situates the user in the middle of a military expedition in Australia. Students that participated were observed to be highly engaged and immersed in the experience. The experience designed in [9] employs a multisensory approach to transport the user to the Antikythera mechanism, where they can explore and interact, while receiving haptic feedback.  
There are a few common limitations that can be identified in projects in the field. First, heightened emotional alertness and increased participatory immersion seem to negatively affect factual retention [10], in part due to the scripted nature of the information presented, which allows little room for self-pacing. Moreover, the experience needs to be concise in order to be fully experienced before physical limitations, such as nausea or fatigue, kick in. Finally, the effort required to produce assets for these experiences is considerable, including adaptation during development [11].  
Some of these problems can be managed by implementing artificial intelligence (AI) agents in the experience. Such an inclusion would facilitate the development of XR experiences, opening up opportunities for a more streamlined process. For example, AI can act as a voice actor and conversational tool. The authors in [12] proposed a dialogue framework that can continuously adapt through communicating with users and itself. ChatGPT 3.5, a popular large language model (LLM) from OpenAI ([[https://chat.openai.com/](https://chat.openai.com/)]([https://chat.openai.com/](https://chat.openai.com/))), has been found to perform reasonably well in contextual narrative generation, albeit with an identifiable style [13]. A crucial issue to consider is the historical and factual accuracy of the AI-generated content, specifically dialogues. Content generated by ChatGPT algorithms, for example, may contain political, racial, and gender biases, as well as hallucinatory facts [14; 15; 16; 17]. These biases and inaccuracies arise because the model learns from vast amounts of data, which inherently include the biases and errors present in the real world. Consequently, when generating responses, the model may replicate these biases and inaccuracies. However, in these works, ChatGPT was not given context as to its role and \"prior experiences\".  
A dependable agent can serve as a grounding factor for the user [18]. The agent's responses to user interaction can also affect the human experience and performance in this context; however, it is not always feasible to employ expert validation on the agent's output [19].  
In this paper, we present an exploration of various problems that are inherent in AI-generated dialogue that can have a detrimental effect on the authenticity of historical XR experiences. Our discussion extends beyond mere identification of these issues, encompassing innovative prompt engineering techniques designed to alleviate them. Our work aims to bridge the gap between current AI limitations and the potential for more accurate and immersive simulations in XR.