AI-enhanced human-machine collaboration for supporting workers with mathematical learning difficulties

Enhancing inclusivity in manufacturing environments is critical for addressing workforce diversity and improving overall productivity, particularly for operators with cognitive challenges such as dyscalculia. Operators with mathematical learning difficulties frequently encounter challenges in manufacturing tasks requiring precise numerical interpretation and measurement, which can limit their effective participation in complex workflows. This study presents a human-machine collaboration approach, specifically tailored to support inclusive manufacturing environments, by integrating computer vision, collaborative robotics, and generative AI technologies. The developed system provides targeted assistance across four key stages of a cable cutting and connector assembly process, utilising image-to-robot coordinate transformation and a natural language interface for intuitive voice interaction. The primary methodological contribution of this work is the innovative combination of AI-driven communication and robotic assistance designed to support the alleviation of cognitive burdens associated with numerical processing tasks. Laboratory-scale testing suggests that the system can enhance usability, efficiency, and accuracy, indicating the potential of advanced human-machine collaborative solutions to promote inclusivity and improve operator support in modern manufacturing environments.