mlsanityData to 14 Oct 2026

Evaluating Usability and Engagement of Large Language Models in Virtual Reality for Traditional Scottish Curling

arXiv 2408.09285 ↗First posted 17 Aug 2024cs.HC cs.AIcited by 4

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Abstract (arXiv)

This paper explores the innovative application of Large Language Models (LLMs) in Virtual Reality (VR) environments to promote heritage education, focusing on traditional Scottish curling presented in the game ``Scottish Bonspiel VR''. Our study compares the effectiveness of LLM-based chatbots with pre-defined scripted chatbots, evaluating key criteria such as usability, user engagement, and learning outcomes. The results show that LLM-based chatbots significantly improve interactivity and engagement, creating a more dynamic and immersive learning environment. This integration helps document and preserve cultural heritage and enhances dissemination processes, which are crucial for safeguarding intangible cultural heritage (ICH) amid environmental changes. Furthermore, the study highlights the potential of novel technologies in education to provide immersive experiences that foster a deeper appreciation of cultural heritage. These findings support the wider application of LLMs and VR in cultural education to address global challenges and promote sustainable practices to preserve and enhance cultural heritage.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 5 of its 70 references that are in the map. First posted on arXiv 17 Aug 2024.

Builds on

5 of 70 references.

References · 5

  1. 2023Can LLM-Generated Misinformation Be Detected?cited by 177
  2. 2023Generative Pre-trained Transformer: A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directionscited by 55
  3. 2023Align on the Fly: Adapting Chatbot Behavior to Established Normscited by 23
  4. 2024Embedding Large Language Models into Extended Reality: Opportunities and Challenges for Inclusion, Engagement, and Privacycited by 14
  5. 2024Response Generation for Cognitive Behavioral Therapy with Large Language Models: Comparative Study with Socratic Questioningcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.