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The 4th industrial revolution has given new opportunities for the development of and use of intelligent decision support systems. With these technological advancements, key concerns focus on gaining a better understanding of the linkage between the technicians\u2019 knowledge and the intelligent decision support systems. The research reported in this study has two primary objectives. (1) To propose a theoretical model that links technicians\u2019 knowledge and intelligent decision support systems, and (2) to present a use case how to apply the theoretical model. The foundation of the new model builds upon two main streams of study in the decision support literature: \u201cdistribution\u201d of knowledge among different agents, and \u201ccollaboration\u201d of knowledge for reaching a shared goal. This study resulted in the identification of two main gaps: firstly, there must be a greater focus upon the technicians\u2019 knowledge; secondly, technicians need assistance to maintain their focus on the big picture. We used the cognitive fit theory, and the theory of distributed situation awareness to propose the new theoretical model called \u201cdistributed collaborative awareness model.\u201d The model considers both explicit and implicit knowledge and accommodates the dynamic challenges involved in operational level maintenance. As an application of this model, we identify and recommend some technological developments required in augmented reality based maintenance decision support.<\/jats:p>","DOI":"10.1007\/s00146-020-01076-x","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T10:06:03Z","timestamp":1601546763000},"page":"817-828","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Collaborating AI and human experts in the maintenance domain"],"prefix":"10.1007","volume":"36","author":[{"given":"Prasanna","family":"Illankoon","sequence":"first","affiliation":[]},{"given":"Phillip","family":"Tretten","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,1]]},"reference":[{"key":"1076_CR1","unstructured":"Ahmadi M, Seneviratne D, Garmabaki A (2017) An approach to symbolic modelling: a railway case study for maintenance recovery level identification. 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