Teaming or friction? Human–AI collaboration with a Multimodal Digital Intelligent Assistant in assembly manufacturing

The transition toward Industry 5.0 promotes human-centred production systems in which digital technologies augment human capabilities rather than replace them. Within this paradigm, Digital Intelligent Assistants have emerged as promising tools to support operators during complex industrial tasks. Moreover, Generative Artificial Intelligence (GenAI), including Large Language Models and Vision Language Models, enables the development of such systems by combining natural language interaction with visual perception. This exploratory mixed-methods pilot study investigates the impact of a multimodal Digital Intelligent Assistant (M-DIA) designed to support operators during a manual assembly process. A controlled laboratory experiment involving 60 participants compared four conditions: multimodal interaction (voice and camera), voice-only interaction, camera-only interaction, and a manual baseline. The analysis considered process performance, perceived cognitive workload, usability and user experience, and operator behaviour. The results show that M-DIA improves assembly quality by reducing product errors compared with the manual condition, although no reduction in total process time is observed due to interaction-related delays. From a human perspective, the assistant reduces perceived cognitive workload, while higher frustration levels emerge in configurations involving voice interaction. Usability and user experience evaluations are overall positive across all experimental conditions. Behavioural observations further indicate that operators maintain an active supervisory role, critically assessing system outputs and avoiding systematic overreliance on the assistant. These findings provide preliminary empirical evidence on the role of GenAI-based multimodal assistants in human-centred manufacturing and highlight key design implications for their industrial deployment.