Exploring LLM-Powered Role and Action-Switching Pedagogical Agents for History Education in Virtual Reality
May 05, 2025 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Zihao Zhu, Ao Yu, Xin Tong, Pan Hui
arXiv ID
2505.02699
Category
cs.HC: Human-Computer Interaction
Citations
3
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
Multi-role pedagogical agents can create engaging and immersive learning experiences, helping learners better understand knowledge in history learning. However, existing pedagogical agents often struggle with multi-role interactions due to complex controls, limited feedback forms, and difficulty dynamically adapting to user inputs. In this study, we developed a VR prototype with LLM-powered adaptive role-switching and action-switching pedagogical agents to help users learn about the history of the Pavilion of Prince Teng. A 2 x 2 between-subjects study was conducted with 84 participants to assess how adaptive role-switching and action-switching affect participants' learning outcomes and experiences. The results suggest that adaptive role-switching enhances participants' perception of the pedagogical agent's trustworthiness and expertise but may lead to inconsistent learning experiences. Adaptive action-switching increases participants' perceived social presence, expertise, and humanness. The study did not uncover any effects of role-switching and action-switching on usability, learning motivation, and cognitive load. Based on the findings, we proposed five design implications for incorporating adaptive role-switching and action-switching into future VR history education tools.
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