Leveraging Large Language Models for Automated XR Instructional Content Generation
Year: 2025
Type: conference-paper
Abstract: This paper presents a study in which authors examine the potential of leveraging large language models to generate instructional content for eXtended Reality environments. Considering the IEEE ARLEM standard as a framework for structuring data, it could be integrated and interpreted by existing authoring tools. In terms of methods, authors have adopted an exploratory approach in testing various strategies. A case study focusing on the use of an eXtended Reality authoring tool for teaching operating procedures is presented. Finally, this exploratory work shows that while simple prompts can produce scenarios with satisfactory quality, imposing a structured schema through more complex prompts leads to less reliable outcomes.
Author: Alex Gabriel
Institution: Équipe de Recherche sur les Processus Innovatifs
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W4413158703