As agile manufacturing expands and workforce mobility increases, the importance of efficient knowledge transfer among factory workers grows. Cognitive Assistants (CAs) with Large Language Models (LLMs), like GPT-3.5, can bridge knowledge gaps and improve worker performance in manufacturing settings. This study investigates the opportunities, risks, and user acceptance of LLM-powered CAs in two factory contexts: textile and detergent production. Several opportunities and risks are identified through a literature review, proof-of-concept implementation, and focus group sessions. Factory representatives raise concerns regarding data security, privacy, and the reliability of LLMs in high-stake environments. By following design guidelines regarding persistent memory, realtime data integration, security, privacy, and ethical concerns, LLMpowered CAs can become valuable assets in manufacturing settings and other industries.
CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Natural language interfaces; • Computing methodologies → Natural language generation; • Information systems → Users and interactive retrieval; • Applied computing → Industry and manufacturing.
Many industries are facing the challenge of how to capture workers' knowledge such that it can be shared, in particular tacit knowledge. The operation of complex systems such as a manufacturing line is knowledge-intensive, especially if the operator must frequently reconfgure it for diferent products. Considering the breadth and dynamic nature of this knowledge, existing solutions for sharing knowledge (e.g., word-of-mouth, issue reports, document creation, and decision support systems) are inefcient and/or resource-intensive. Conversational user interfaces are an efcient way to convey information that mimics the way humans share knowledge; however, we know little about how to design them specifcally for this purpose, especially regarding tacit knowledge. In this work, my main goal is to investigate how a cognitive assistant can be designed to facilitate (tacit) knowledge transfer between users of dynamic complex systems. I aim to achieve this by outlining the design requirements, challenges, and opportunities in factories; by collaboratively designing, implementing, and evaluating a cognitive assistant for sharing knowledge; studying the efects of design characteristics on aspects such as user experience; and fnally, creating a set of design guidelines.
CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; Interactive systems and tools; Natural language interfaces.
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