Publications

@inproceedings{alex_leveraging_2025,

title = {Leveraging Large Language Models for Automated XR Instructional Content Generation},

author = {G. Alex},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105015398440&doi=10.1109%2FICE%2FITMC65658.2025.11106622&partnerID=40&md5=c125d3b7e58cfff4c24a9b15bb615912},

doi = {10.1109/ICE/ITMC65658.2025.11106622},

isbn = {9798331585341 (ISBN)},

year  = {2025},

date = {2025-01-01},

publisher = {Institute of Electrical and Electronics Engineers Inc.},

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. © 2025 Elsevier B.V., All rights reserved.},

keywords = {Artificial intelligence, Authoring Tool, Case-studies, Engineering education, Extended reality, IEEE Standards, Language Model, Large language model, Learning systems, Ontology, Ontology's, Simple++},

pubstate = {published},

tppubtype = {inproceedings}

}