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This work-in-progress research paper describes the pilot work in a study seeking to gain further insight on the relationships between intuition, expertise, and experience through a better understanding of how intuition is applied in engineering problem solving. Individuals who have attained a high level of expertise, exhibit characteristics of intuitive decision making (Dreyfus & Dreyfus, 1980). The development of expertise (Dreyfus &; Dreyfus, 1980; Seifert et al., 1997) and intuition (Authors, 2019; Authors, 2023) are heavily influenced by experience. Engineering intuition can be summarized as a subconscious problem-solving skill that is based on previous experience (Authors, 2023). In this work, we will be using Cognitive Task Analysis (CTA) to examine the use of intuition in engineering problem solving. CTA is a class of observational protocols that surface tacit knowledge through engaging experts with a task (Crandall, 2006). The purpose of CTA is to capture how the mind works through three primary aspects: knowledge elicitation, data analysis, and knowledge representation. As best CTA practices use multiple methods, we will use three methods for this analysis, 1) Simulation Interviews where participants are given a simulated engineering problem and asked to speak out loud to describe their process in approaching the problem, 2) Critical Decision Method (Klein, 1989) where a retrospective interview probes the decisions made during the simulation interview, and 3) Knowledge Audit Method (Taheri et al., 2014) which further guides our probing questions to identify types of knowledge used, or not used, during the simulated problem solving experience. These three techniques are applied to collect data on participants' problem solving. To develop the problems for the Simulation Interviews, we have first conducted pilot work using just the Critical Decision Method and Knowledge Audit Method. As part of the Critical Decision Method, participants will select a non routine problem-solving incident, construct an incident timeline, identify decision points for future probing, and then probe these decisions using the Knowledge Audit Method. This method allows us to determine realistic, practice-based problems for the Simulation Interview, why the participant makes certain decisions, and how their educational background and on the job training influenced their decision making process. The anticipated outcomes of this research are to expand engineering education through a better understanding of engineering intuition and to provide a foundation for the explicit application of intuition in engineering problem solving. These insights can be beneficial for creating educational interventions that promote intuition development and introduce real-world engineering practices in the classroom. This in turn can promote metacognition in engineering students by creating pathways to expertise development, as well as boost confidence and support retention (Metcalfe & Wiebe, 1987; Bolton, 2022; Authors, 2021; Authors, 2023). Additionally, insights into intuition can be beneficial in onboarding new hires who may have more expertise development, agility, and adaptability to the technical landscape in the engineering workforce. References: Authors. (2021). Authors. (2019). Authors. (2023). Bolton, C. S. (2022). What Makes an Expert? Characterizing Perceptions of Expertise and Intuition Among Early-Career Engineers [Undergraduate Honors Thesis, Bucknell University]. Lewisburg, PA. Crandall, B., Klein, G. A., &; Hoffman, R. R. (2006). Working minds: A practitioner's guide to cognitive task analysis. MIT Press. Dreyfus, S. E., & Dreyfus, H. L. (1980). A Five-Stage Model of the Mental Activities Involved in Directed Skill Acquisition. Klein, G. A, Calderwood, R., and Macgregor, D. (1989). Critical decision method for eliciting knowledge, IEEE Transactions on systems, man, and cybernetics, 19(3), 462-472. https://doi.org/10.1109/21.31053 Metcalfe, J., & Wiebe, D. (1987). Intuition in Insight and Noninsight Problem Solving. Memory & Cognition, 15(3), 238-246. https://doi.org/10.3758/BF03197722. Seifert, C. M., Patalano, A. L., Hammond, K. J., & Converse, T. M. (1997). Experience and expertise: The role of memory in planning for opportunities. In P. J. Feltovich, K. M. Ford, & R. R. Hoffmanm (Eds.), Expertise in Context (pp. 101-123). AAAI Press/ MIT Press. Taheri, L., Che Pa, N., Abdullah, R., & Abdullah, S. (2014). Knowledge audit model for requirement elicitation process. International Scholarly and Scientific Research & Innovation, 8(2), 452-456. 

This work in progress paper describes preliminary findings from interviews intending to develop a definition of and method for measuring “engineering intuition.” Engineers are asked regularly in their profession to judge situations and predict or estimate results in order to minimize the potential for error. The need for this ability has been amplified with the pervasiveness of computer-aided problem solving in engineering. It is now mandatory for practicing engineers to quickly and accurately evaluate software results as part of the problem-solving process. We hypothesize that the ability to undertake such actions is heavily influenced by discipline-specific intuition, which has been previously explored in the disciplines of nursing and business management. The following study presents preliminary results attempting to define the construct of “engineering intuition.” Semi-structured interviews with practicing nurses, business managers, and engineers were conducted using: 1) implicit discussion around intuition informed by literature, and 2) critical incident technique, i.e., explicit discussion around the concept of intuition. Each interview sought to identify practitioner decision-making and problem-solving processes on the job. The combined dataset and supporting literature are planned to be used as the basis of our future work, which ultimately aims to develop a psychometrically tested instrument capable of accurately measuring engineering intuition. Dissemination of these preliminary results are intended to elicit feedback on our methodologies and findings before moving to the second phase of our research study.