In a large industrial setting, a large amount of intangible knowledge in terms of experiences and intuitions is confined to experts, making it inaccessible for organisations to capitalise on by improving their workflow. If harnessed, experts’ experiences and practices could help novice employees do their work efficiently. Understanding experts’ experiences and intuition could help organisations maintain critical knowledge, known as tacit knowledge. Through tacit knowledge, organisations could aggregate learnings and co-operate to produce faster and more efficient results.
To further advance the concept of tacit knowledge, Swansea partnered with British Telecom (BT), a UK-based telecom service provider, to capture physiological data points and evaluate the possibility of AI in the collective management and distribution of tacit knowledge. Human-centred design methods were applied to identify the characteristics of experts and non-experts in lock-picking and Lego car robots. The methods used to capture gaze and physiological data, such as eye tracking technology, and wristband sensors were non-intrusive in nature. Gaze data was used for analysis as the physiological data displayed calibration error and limited correlation with gaze data.
The study indicates that user gaze in relation to the area of interest (AOI) provides an objective tool for measuring visual patterns. It also demonstrates the identification of experts and non-experts through their average fixation period and quality of attention. The application of the study lies in designing and developing machines that promote the growth of tacit knowledge in humans by capturing their work and workflow along with the relevant tacit knowledge. It is imperative to highlight and acknowledge that tacit knowledge has its dependence on the working environment, its interactions, and feedback.
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