Lessons Learned from Designing and Evaluating CLAICA
A Continuously Learning AI Cognitive Assistant
S. Kernan Freire (TU Delft - Internet of Things)
Evangelos Niforatos (TU Delft - Internet of Things)
Chaofan Wang (TU Delft - Human-Centred Artificial Intelligence)
Santiago Ruiz-Arenas (Universidad EAFIT, TU Delft - Internet of Things)
Mina Foosherian (University of Bremen)
Stefan Wellsandt (University of Bremen)
Alessandro Bozzon (TU Delft - Human-Centred Artificial Intelligence)
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Abstract
Learning to operate a complex system, such as an agile production line, can be a daunting task. The high variability in products and frequent reconfigurations make it difficult to keep documentation up-to-date and share new knowledge amongst factory workers. We introduce CLAICA, a Continuously Learning AI Cognitive Assistant that supports workers in the aforementioned scenario. CLAICA learns from (experienced) workers, formalizes new knowledge, stores it in a knowledge base, along with contextual information, and shares it when relevant. We conducted a user study with 83 participants who performed eight knowledge exchange tasks with CLAICA, completed a survey, and provided qualitative feedback. Our results provide a deeper understanding of how prior training, context expertise, and interaction modality affect the user experience of cognitive assistants. We draw on our results to elicit design and evaluation guidelines for cognitive assistants that support knowledge exchange in fast-paced and demanding environments, such as an agile production line.
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