Work

Towards Enhancing Industrial Training Through Conversational AI

Year: 2025

Type: conference-paper

Abstract: 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.

DOI: 10.1145/3719160.3737643

Authors: Miruna Maria Vasiliu, Renan Guarese, Jonas Jaatinen, Fabian Johnson, Benjamin Edvinsson, Mario Romero

Institutions: KTH Royal Institute of Technology, University of Twente, AstraZeneca (Sweden), Linköping University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Quality education

Open Access status: green

Funder: AstraZeneca

Award: G394514893