Tacit Knowledge Based Acquisition of Verified Machining Data

Schmid, Johannes; Brillinger, Markus; Pichler, Rudolf; Wurzinger, Johannes; Ciumasu, Radu

doi:10.1109/icitm48982.2020.9080346

Cited by 5 publications

(1 citation statement)

References 18 publications

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“…Researchers have manually acquired tacit knowledge through human motion capture, videos, and feld interviews with experts and beginners in the manufacturing industry [17] -a process that requires skilled analysts. If data is available that details the actions of workers, data analysis techniques can be used to identify tacit knowledge [39], [46]. However, these techniques do not enable the knowledge holder to describe the reasoning behind their actions or add additional details, as an expert might when teaching a novice.…”

Section: Introductionmentioning

confidence: 99%

The Human Factors of AI-Empowered Knowledge Sharing

Freire

2023

Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

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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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Section: Introductionmentioning

confidence: 99%