Conference Session

WORKSHOP III: From Ideas to Action: Integrating Entrepreneurial Mindset in FYE Programs

Collection

FYEE 2025 Conference

Authors

Kaitlin Mallouk, Rowan University; J. Blake Hylton, Ohio Northern University; Jack Bringardner, Colorado School of Mines; Krista M Kecskemety, The Ohio State University; Cassie Wallwey, Virginia Polytechnic Institute and State University; Andrew Charles Bartolini, University of Notre Dame

Tagged Topics

FYEE 2025

that can be applied to any activity to facilitate EM integration‬ ‭●‬ ‭A strategy to address stakeholder concerns‬‭AI Acknowledgement‬‭This proposal was originally written as two separate proposals by the workshop presenters with‬‭no support from generative AI. ChatGPT was used to combine the two original proposals into a‬‭single proposal and the resulting description was edited for clarity and accuracy.‬

Conference Session

GIFTS II

Collection

FYEE 2025 Conference

Authors

Osman Sayginer, Temple University; Cory Budischak, Temple University

Tagged Topics

Diversity, FYEE 2025

-a = 0.25 -x = 4 ::Numeric Precision:: -x = 0 What is the density of water at room temperature in kg/m³? ::Numeric Precision:: [!Numeric!] [1000+-5] How many km in a marathon? ::Numeric Range:: [!Numeric!] [42.2+-0.1] What is the typical range of efficiency (%) for a modern gas turbine? ::Numeric Range:: [!Numeric!] [30 40] Normal body temp in Celsius? [!Numeric!] [36.5 37.5]Figure 2. Example of an input prompt used to generate quiz questions (left) and the resultingoutput generated by ChatGPT (right).Preliminary Results and DiscussionInitial trials

Conference Session

Full Papers IV

Collection

FYEE 2025 Conference

Authors

James Nathaniel Newcomer, Virginia Polytechnic Institute and State University; David Gray, Virginia Polytechnic Institute and State University; Alice Hyunna Noble, Virginia Polytechnic Institute and State University; Devin Erb, Virginia Polytechnic Institute and State University; Annabel Bass, Virginia Polytechnic Institute and State University

Tagged Topics

FYEE 2025

capture how frequently pairs of codes appeared within the samestudent response. For this analysis, we isolated the 24 codes related to students’professional goals and examined their relationship to students’ intended majors. Majorswith limited representation in the dataset—including biological systems engineering,building construction, construction engineering management, ocean engineering, andmaterial science engineering—were excluded to avoid unreliable clustering. We thenused k-means clustering, assisted by ChatGPT, to identify patterns in the distribution ofgoal codes across majors. Based on silhouette scoring (= 0.193), six clusters wereidentified. For each cluster, we extracted the most frequent goal codes associated withthe majors it