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We all have moments when we are struck by a “gut feeling” or a “sixth sense” about something. It could pertain to a relationship or task at work. That sense can be broadly termed intuition. Intuitive decisionmaking is an essential characteristic of individuals who have attained a certain level of expertise. The development of expertise and intuition are heavily influenced by experience. Engineering intuition is defined as an experience-informed skill subconsciously leveraged in problem solving by engineering practitioners when under pressure from constraints such as lack of time. Practicing engineers use and develop intuition regularly on-the-job, but the use of intuition is often discouraged in undergraduate education. The disconnect between intuition’s use in engineering practice and in education, coupled with our limited knowledge of the relationship between intuition, expertise, and experience, presents an important gap in our existing understanding of engineering problem solving and future workforce preparation. Through this Research in the Formation of Engineers (RFE) grant, we seek to address this gap by examining the application of intuition by engineering practitioners to generate knowledge that promotes professional formation and development of a stronger engineering workforce. 

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.