The Right Voice for the Right Task: Exploratory Insights on Persona and Voice Embodiment in Conversational AI for Industrial Training

As industries increasingly adopt predictive maintenance (PdM) strategies under Industry 4.0 and 5.0 frameworks, there is a growing need for user-centered technologies that support industrial training. Conversational AI (CAI) has proven effective in educational settings, however its potential in industrial settings, where higher precision and reliability are required, remains underexplored. Through this study, we explore the potential of CAI, particularly speech-based digital intelligent assistants (DIAs), in supporting industrial training in a pharmaceutical context. Specifically, this study investigates how two design dimensions–AI persona (Expert Operator vs. Machine) and voice embodiment (Diegetic vs. Disembodied)–affect usability, perceived workload, trust, and task performance during manual operation of industrial machinery. A functional prototype using the OpenAI RealTime API was developed and evaluated through a 2×2 within-subjects user study with nine domain-relevant participants: novice and expert operators. Based on semi-structured interviews supported by self-reported data from structured questionnaires, results indicate a nuanced relationship between DIA configurations and different users or contexts. The Expert Operator persona was generally preferred for trust and engagement, while the Machine persona provided clearer instructions for some users. Diegetic voices were perceived as more intuitive, but disembodied voices improved focus and privacy. These findings highlight the need for flexible, user-adaptive CAI designs that accommodate individual preferences and experience levels. This study offers initial insights into the user-centric design of such systems and proposes considerations for persona and embodiment design that support the autonomy of the trainees.