Despite the growing prominence of Artificial Intelligence (AI) chatbots used in education, there remains a significant gap in our understanding of how interface design elements, particularly avatar representations, influence learning experiences. This paper explores the impact of different AI chatbot avatar representations on students’ learning experiences through a mixed-methods within-subjects study, where participants interacted with three distinct types of AI chatbot interfaces with a common large language model (LLM) over a 14-week university course. Our findings reveal that preferences vary according to factors such as learning habits and learning activities. Avatar design also exhibits affordances for specific prompting behaviors, while the perceived human touch influenced learning experiences in nuanced ways. Additionally, real-world relationships with the individuals behind deepfakes influence these experiences. These insights suggest that the thoughtful integration of diverse avatar representations in AI chatbot systems for different learners and settings can greatly enhance learning experiences.

In this paper, we present an approach for out-of-core dynamic streaming of a virtual scene. We use the traditional client/server architecture, where the main responsibility of the client application is visualization and interaction, and the main responsibility of the server is accepting and serving requests from the client application. The client will query geometry in the camera proximity to the server and the server will stream geometry (in CityGML format) to the client application. We implement the client application using Unity game engine. Performance comparison between traditional loading and our dynamic streaming are provided. We also show the scalability advantage of our work.