Towards Enhancing Industrial Training Through Conversational AI
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.
Authors
- Benjamin Edvinsson (ORCID: https://orcid.org/0009-0001-9108-4902)
- Mario Romero (ORCID: https://orcid.org/0000-0003-4616-189X)
- Marius Vasiliu (ORCID: https://orcid.org/0009-0006-4963-855X)
- Renan Guarese (ORCID: https://orcid.org/0000-0003-1206-5701)
- Fabian Johnson
- Jonas Jaatinen (ORCID: https://orcid.org/0009-0001-0123-0081)
Institutions
- Linköping University (SE)
- AstraZeneca (Sweden) (SE)
- KTH Royal Institute of Technology (SE)
- University of Twente (NL)
Publication Details
- Published
- 2025-07-05
- DOI
- https://doi.org/10.1145/3719160.3737643
- Citations
- 4
- Primary Topic
- AI in Service Interactions
- Type
- article
- Field-Weighted Citation Impact
- 9.92
Funders
- AstraZeneca