Towards Enhancing Industrial Training Through Conversational AIShow others and affiliations
2025 (English)In: CUI '25: Proceedings of the 7th ACM Conference on Conversational User Interfaces, Association for Computing Machinery (ACM) , 2025Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]
Conversational AI (CAI) has proven effective in educational settings, however its potential in industrial training, where higher precision and reliability are required, remains under-explored. This work-in-progress paper proposes a study to examine how AI persona design (Machine vs. Expert Operator) and voice embodiment (Diegetic vs. Disembodied) influence cognitive load, task efficiency, and usability in industrial training. By training a large language model (LLM) on Standard Operating Procedure (SOP) data, this project aims to develop a CAI assistant that provides real-time, easy-to-access information during task execution, in an attempt to enhance training efficiency and reduce reliance on text-heavy manuals through a user-centered approach.
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2025.
Keywords [en]
Natural language interfaces
National Category
Natural Language Processing
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-367516DOI: 10.1145/3719160.3737643Scopus ID: 2-s2.0-105011598225OAI: oai:DiVA.org:kth-367516DiVA, id: diva2:1984901
Conference
7th ACM Conference on Conversational User Interfaces, CUI ’25, July 08–10, 2025, Waterloo, ON, Canada
Note
QC 20250729
2025-07-182025-07-182025-08-07Bibliographically approved