Displaying all 4 results
- Conference Session
- Minorities in Engineering Division(MIND) Technical Session 2
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Aldo R Pinon Villarreal, Angelo State University
- Tagged Topics
- Diversity
- Tagged Divisions
- Minorities in Engineering Division(MIND)
personalexperience relating to one of the topics covered in the course materials. The second high contextquestion was a fill in the gap series of questions in which they needed to identify the name of theconcept or equation after providing a description of a real-case scenario. See Figure 1. To deterstudents from copying or collaborating with others, a total of three different exam versions werereleased and the included numeric problems were not previously published so they coul d not befound online. ChatGPT had not been released yet so it had no effect on this investigation, but itwould need to be addressed for future applications.Figure 1. Excerpt from a fill-in-the gap question series to identify the name of the concept orequation by providing them a
- Conference Session
- Voices of Diversity: Perspectives and Experiences in STEM Education
- Collection
- 2024 ASEE Annual Conference & Exposition
- Authors
- Claire MacDonald, The University of Texas at El Paso; Palvi Aggarwal, The University of Texas at El Paso; Xiwei Wang, Northeastern Illinois University; Yun Wan; Shebuti Rayana, The State University of New York at Old Westbury; Rudy Caraballo; Sherrene Bogle, Cal Poly Humboldt
- Tagged Divisions
- Minorities in Engineering Division(MIND)
towards chatGPT from social media platforms.Rudy CaraballoDr. Sherrene Bogle, Cal Poly Humboldt Dr. Sherrene Bogle is a Fulbright Scholar and alumna of the University of Georgia, USA, where she earned her PhD in Computer Science. She is currently an Associate Professor of Computer Science and Program Lead for the BS Software Engineering at Cal Poly Humboldt. Dr. Bogle has a passion for sharing and helping students to improve the quality of their lives through education, motivation and technology. She has published two book chapters, two journal articles and several peer reviewed conference papers in the areas of Machine Learning, Time Series Predictions, Predictive Analytics, Multimedia in Education and E-Learning
- Conference Session
- Minorities in Engineering Division(MIND) Technical Session 11
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Haya Alshayji, Pennsylvania State University; Deja Workman, Pennsylvania State University; Swapnika Dulam, Pennsylvania State University; Lauren A Griggs, The Pennsylvania State University; Dixon Zor, Pennsylvania State University; Christopher L Dancy, The Pennsylvania State University, University Park
- Tagged Topics
- Diversity
- Tagged Divisions
- Minorities in Engineering Division(MIND)
://doi.org/10.1145/3375627.3375868[7] J. Borenstein and A. Howard, “Emerging challenges in AI and the need for AI ethicseducation,” AI and Ethics, 2021[8] J. Borenstein and A. Howard, “Emerging challenges in AI and the need for AI ethicseducation,” AI Ethics, vol. 1, pp. 61–65, 2021. doi: 10.1007/s43681-020-00002-7. [Online].Available: https://doi.org/10.1007/s43681-020-00002-7[9] S. Wang, T. Xu, H. Li, C. Zhang, J. Liang, J. Tang, P. S. Yu, and Q. Wen, “Large LanguageModels for Education: A Survey and Outlook,” arXiv preprint, vol. abs/2403.18105, 2024.[Online]. Available: https://arxiv.org/abs/2403.18105[10] E. Kasneci et al., “ChatGPT for Good? On Opportunities and Challenges of Large LanguageModels for Education.” Center for Open Science, 2023
- Conference Session
- Minorities in Engineering Division(MIND) Technical Session 1
- Collection
- 2025 ASEE Annual Conference & Exposition
- Authors
- Nia A. Keith, Purdue University College of Engineering; Jacqueline E McDermott, Purdue University at West Lafayette (COE)
- Tagged Topics
- Diversity
- Tagged Divisions
- Minorities in Engineering Division(MIND)
includedstudent experiences, feedback on program sessions, and suggestions for improvement. Next,feedback was separated by years (2017-2019, 2020-2023, and 2024) based on the different EarlyDiscovery program formats and input into Open AI software (ChatGPT), with the command ofidentifying the most frequently used words. These frequently used words were inserted into aword cloud generator website (https://www.freewordcloudgenerator.com/) to visually representthese terms. The final word cloud result provides a visual of the student feedback and keytakeaways from their experiences.Results and DiscussionThe three different Early Discovery program formats have their own goals, frameworks,benefits, and limitations (RQ1)To determine which Early Discovery