precision in controlled environments, focusing on improving model accuracy.However, the emphasis on machine learning also reflects a broader gap in addressing otheraccessibility challenges, particularly in contexts where communication is not the only barrier.Large Language Models (LLMs) such as ChatGPT and chat bots are other technology that couldbe explored to enhance communication accessibility for the hearing impaired [56], however thelack of tested application of LLMs to address accessibility for the hearing impaired may be, atleast in part, explained by how recently LLMs became available to the public.Limited Focus on Classroom AccessibilityDespite the wide range of technology explored, there is a noticeable dearth of studies aimedspecifically
://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
.[28] Kapor Center. Culturally responsive-sustaining computer science education: A framework, 2021. URL https://kaporfoundation.org/publications/.[29] OpenAI. ChatGPT, 2024. URL https://chatgpt.com.[30] Ryan L Boyd, Ashwini Ashokkumar, Sarah Seraj, and James W Pennebaker. The development and psychometric properties of LIWC-22. Technical report, University of Texas at Austin, 2022. URL https://www.liwc.app/.[31] Matthew L. Newman, James W. Pennebaker, Diane S. Berry, and Jane M. Richards. Lying Words: Predicting Deception from Linguistic Styles. Personality and Social Psychology Bulletin, 29(5):665–675, May 2003. ISSN 0146-1672, 1552-7433. doi: 10.1177/0146167203029005010. URL http://journals.sagepub.com/doi/10.1177
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
revolution workforce needs,” in 2023 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2023, pp. 271–276.[27] J. White, Q. Fu, S. Hays, M. Sandborn, C. Olea, H. Gilbert, A. Elnashar, J. Spencer-Smith, and D. C. Schmidt, “A prompt pattern catalog to enhance prompt engineering with chatgpt,” arXiv preprint arXiv:2302.11382, 2023.[28] P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. K¨uttler, M. Lewis, W.-t. Yih, T. Rockt¨aschel et al., “Retrieval-augmented generation for knowledge-intensive nlp tasks,” Advances in Neural Information Processing Systems, vol. 33, pp. 9459–9474, 2020.[29] L. Shani, A. Rosenberg, A. Cassel, O. Lang, D. Calandriello, A. Zipori, H. Noga, O. Keller, B. Piot, I. Szpektor et
ideas in diverse manners, reviewing related literature in the area ofstudy, discussing assignments with lecturers, and using editors and academic social media likeResearchGate, Google Scholar, and YouTube, to mention a few enhanced academic writings. Inaddition, using technology and artificial intelligence AI tools (ChatGPT, Grammarly, and so on)helps overcome these writing challenges.The absence or lack of a proper understanding of academic writing may cause respondents to applytheir preexisting assumptions, opinions, and methods that have provided them with confidence andreliability when faced with academic challenges like writing a research paper [49]. According toCasanave and Hubbard, [50], faculty members failed to provide students with
. Students generated a wall ofideas, with over three hundred ideas written on brightly colored sticky notes. For the initialideation round, students were asked to think of societal problems without the assistance of theirphones or computers. After they seemed exhausted thinking on their own, with 5-10 ideas each,they were next directed to use available resources to gather ideas. Facilitators suggested thatstudents review UN Sustainable Development Goals and explore global grand challenge lists.In the third ideation phase, students were guided to use generative AI applications and to recordand share their iteration process in prompting. The decision to support the exploration of productideas with ChatGPT was not made lightly. Aligned with
and biases that seepinto the design of products and their effect on different populations and society at large.Increasing the representation of historically marginalized populations in the engineering pipelineand into the workforce is crucial in creating a more equitable future for all people.VI: AcknowledgementsThis project is being supported through an internal grant from the university president’s office tofoster innovation. ChatGPT was used for editing earlier drafts of this paper. Also, we wish toacknowledge several colleagues Drs. Kirstie Plantenberg, Michael Santora and Kenneth Lamb ofUniversity of Detroit Mercy, who contributed in various ways to the project discussed here.References[1] “Transforming Undergraduate Engineering
time.Holly mentions “doing research” a few times throughout her interview but is vague aboutwhat this entails. When pushed for details, she describes relying primarily on Google andGoogle Scholar. She sees Google as most helpful for finding what she terms as “hard facts,”such as how an air-based cooling system works. She uses Google Scholar to find morescholarly sources to supplement her work, but finds that the language is often “veryscientific” and difficult to understand. She also mentions using ChatGPT to help explainthings in simpler terms if her tutor is not available to answer questions. She seems generallyhappy to ask questions of her lecturers and her classmates, but is reluctant to admit shedoesn’t know something in front of people she
. Inparticular, natural language processing (NLP) a subset of gen-AI, enables computers to quicklyparse and understand text by identifying the meaningful parts of sentences [34]. Since the releaseof ChatGPT and similar chatbots, engineering education researchers have explored diverse usecases of NLP, including for analyzing student writing and assignments, examining curriculums,research data processing, student support, and assessment [35], [36], [37]. Recent work by ourresearch group [38] has also demonstrated the potential for NLP to aid qualitative thematicanalysis by expediting the codebook generation process. Importantly, these efforts takeadvantage of how NLP handles semantically and syntactically different text by identifyingpatterns between word
towardsthe Society 5.0 global vision. Coupled with the use of conscious, ethical Artificial Intelligence tools (ChatGPT, JasperAI, Copilot, Gemini, etc.) and learning modalities (active/experiential/inquiry-driven, flipped-classroom, etc.) willempower students to individualize learning experiences/outcomes. However, e-learning must be supplemented byopen discussions [13], and project-based/textbook-based learning, especially for foundational subjects. Withinchemical engineering, core subjects and topics like calculus, transport phenomena, chemical thermodynamics,separation processes, and plant/process design (undergraduate capstone) must be taught through a mix of pedagogicalstrategies. Our results reveal an increase (especially since 2017
with participants in my research and to acknowledge thebiases I bring. From my early struggles with homesickness in first year, to my passion foroutreach and advocacy developed through NSBE, to finally securing my first internship in theOil Sands during my master’s degree which I felt ultimately validated my identity as an engineer,my career pathway has been shaped and informed by the experiences in my undergraduatedegree. These reflections ground me in focus of my PhD research: to illuminate the factorsshaping diverse career paths in engineering and to foster environments where all students canthrive.1 The author identified she used ChatGPT as part of her writing process for this section to synthesize similar writingsshe had previously done
published an ASEE conference paper last year on the effects of ChatGPT on student learning in programming courses. With over seven years of experience teaching Computer Science courses, she is currently a faculty member at Embry-Riddle Aeronautical University’s Department of Computer, Electrical, and Software Engineering, where she teaches computer science courses.Dr. Luis Felipe Zapata-Rivera, Embry-Riddle Aeronautical University Dr. Luis Felipe Zapata-Rivera is an Assistant Professor at Embry Riddle Aeronautical University. He earned a Ph.D. in Computer Engineering at Florida Atlantic University, in the past worked as an assistant researcher in the group of educational Technologies at Eafit University in Medellin
were reviewed using ChatGPT 4o as asupplementary tool. When minor discrepancies were identified, the first author revised theChinese translation and discussed the changes with the colleagues until all disagreementswere resolved.In addition to the original EBAPS items, four additional questions, shown in Table 4, wereincluded to explore students’ cognitive patterns under the influence of naïve dialecticism[17].The first two items regarding attitudes toward contradictions were adapted from theDialectical Self Scale[19], an instrument designed to measure dialectical thinking. The othertwo items were created by one of the authors to represent the remaining key components ofnaïve dialecticism: the “Principle of Relationship or Holism” (item iii
content generated from ChatGPT 4.0 (Sept. 2, 2024)and edited by the lead author, are showcased in Figure 2. We do not suggest that this is anexhaustive list of higher education career advising models, but this information offers somerelevant insights upon which to understand and consider different approaches to career advising. Figure 2: Some of the career advising models we see across higher education. This is not an exhaustive list or representation. Many existing career advising models combine elements and features from various of these models.III. THEORETICAL FRAMEWORKS GUIDING WHOLE STUDENT DEVELOPMENTAs an engineering education researcher, the lead author (Pierrakos) has been an NSF-fundedprincipal investigator
education faces, and manyorganizations face, in recruiting diverse talent is also known. According to ChatGPT 4.0(September 2, 2024) and edited to be represented in a figure format (Figure 1), we highlight justsome of the challenges that hinder organizations from building diverse teams. Some of thesechallenges that hinder higher education and hinder engineering education too include: • Biases in Recruitment Processes • Biased Institutional Barriers and Practices • Misalignment of Goals and Practices • Resistance to Change • Company Culture and Lack of Inclusivity • Resource Constraints to Implement Effective Strategies • Lack of Diversity