secured multiple grants for innovative projects. A senior member of IEEE, he actively contributes to the field through publications and conference presentations. ©American Society for Engineering Education, 2025 Case Studies of ChatGPT for Embedded Systems TeachingAbstractThe rise of AI technology, particularly Generative AI, has significantly transformed the landscapeof higher education. Generative AI, such as ChatGPT, has been extensively studied in fields likeComputer Science to assess its effectiveness in enhancing learning. However, its impact on morespecialized areas, such as bare-metal embedded systems, remains underexplored. Bare-metalembedded systems, which include hardware (e.g
Paper ID #48446BOARD # 78: Student Use of ChatGPT and Claude in Introductory EngineeringEducation: Insights into Metacognition and Problem-Solving PatternsDr. Anthony Cortez, Point Loma Nazarene University Anthony Cortez is currently an Assistant Professor in the department of Physics and Engineering at Point Loma Nazarene University. He received his BS in Physics from University of California San Diego (UCSD). He went on to complete his MS and Ph.D. in Mechanical Engineering from University of California Riverside (UCR). His research interests include technology as a tool in the classroom, high temperature superconductivity
Engineering Economy CoursesAuthor: Hamed SamandariAffiliation: University of Massachusetts DartmouthAbstractThe rapid development of Artificial Intelligence (AI) presents both opportunities and challengesfor engineering education. As AI tools like ChatGPT become more accessible, studentsincreasingly use them to complete assignments, prompting educators to evaluate whether tointegrate AI as a learning aid or restrict its usage. This study explores the potential of AI toenhance student learning outcomes in an engineering economics course. Specifically, we utilizeChatGPT, which provides detailed explanations of economic theories, guidance onmicroeconomic principles, example problems, and real-world economic
Master’s in Advanced Computing and a Bachelor’s in Computer Science, Abiola has expertise in data science, cybersecurity, networking, business analysis, and system administration. A member of ASEE,IEEE who is passionate about STEM education to introduce K1-12 students to computing/ engineering skills and digital literacy.OLUWATOYOSI OYEWANDE, Morgan State University ©American Society for Engineering Education, 2025Perception of the Impact of Artificial Intelligence on EducationAbstractThis work-in-progress paper explores the perceptions of students and educators regarding theimpact of Artificial Intelligence (AI) on education, specifically before and after the release ofOpenAI’s ChatGPT. Using a mixed
Capabilities to Perform Specific TasksIntroductionGenerative AI (GAI) tools like ChatGPT and Copilot can quickly prepare polished, fiveparagraph essays and clever limericks about any given topic, but can they multiply seventwo-digit numbers? Or answer a question from the Fundamentals of Engineering exam? Or tellyou what the image in a “connect-the-dots” puzzle is? GAI tools are designed to be able toproduce human-like language responses to given prompts, but performance varies depending onthe nature of each task. To further complicate the evaluation of GAI performance, each tool (e.g.ChatGPT, Copilot, Gemini) has its own process for generating responses, and these processescan evolve rapidly – with success varying across tools
research-based assignments has been exploredless. This study investigates the efficiency and fairness of using AI, specifically ChatGPT, tograde theoretical understanding and research paper assignments in undergraduate and graduatecourses. The research was conducted in two phases. In the first phase, we assessed ChatGPT'sperformance in grading assignments, focusing on time efficiency, consistency, and gradingpatterns. We compared AI-assisted grading with traditional human grading methods in thesecond phase. We then analyzed variations in scores, potential biases, and feedback'sperceived usefulness. We conducted surveys to gather perceptions from both students andeducators regarding AI-based grading.The results indicated that AI-assisted grading
support all students, with particular attention to hearing-impaired learners inAME308: Computer-Aided Design (CAD). Hearing-impaired students received standard OSASaccommodations, including 1.5x extended time for homework/exams and direct TA support,while AI tools were adopted to address pedagogical gaps. The course’s dynamic nature—evolvingthrough student interactions—rendered traditional notes inadequate. To bridge this gap, lecturerecordings and AI-generated summaries (created using ChatGPT) were provided to all students,benefiting those with hearing impairments, temporary absences, or diverse learning needs.The approach leveraged ChatGPT to transform Zoom subtitles and lecture materials intostructured previews, reviewed by TAs for accuracy
Engineering Education, 2025 Educators’ Perspectives on the use of Generative AI Tools in Teaching and Educational Research in EngineeringAbstractSince the release of ChatGPT by OpenAI in November 2022, the integration of generative AI(genAI) into teaching and education has gained significant attention and experienced rapid growthwithin university engineering programs. This paper investigates the application of genAI inengineering education and research, focusing on the potential benefits and challenges of itsadoption. Specifically, the study: A) Analyzes how educators and students perceive and utilizegenAI and ChatGPT in engineering education; B) Explores the advantages, challenges, andlimitations associated with these technologies
designs in mechanical engineering [65], making ethical choices during prototypingin time-sensitive situations such as hackathons [66], and learning disciplinary skills needed fordesign projects through personalized learning [67]. Lastly, a handful of papers explore howGenAI tools can give timely, relevant, and epistemic feedback during design. One exampleis the use of ChatGPT to analyze progress reports, instrumental to team collaborations, byrecommending readability improvements and clarifying complex ideas [68].3.3 PositionsOur review found 33 position papers revealing diverse viewpoints on its integration, eth-ical considerations, and potential applications of GenAI in EE. Specifically, these papersare where authors argue their stance on or
ThermodynamicsAbstractGenerative artificial intelligence (GenAI) has become ubiquitous. Convincing languagecomplemented by constant modifications and upgrades have made GenAI models, such asOpenAI’s ChatGPT, an appealing tool to address complex problems. According to a survey byIntelligent.com nearly a third of college students in AY 2022-2023 used ChatGPT for schoolworkand 77.4% of them were likely to recommend using it to study to another student. Despite theirappeal, these models have proven flawed in answering technical prompts. Their convincinglanguage may entice the user to trust the responses without verifying them. For example, theauthors failed to retrieve accurate thermodynamics properties of some common substances fromthree publicly available models (OpenAI’s
Biomechanics, Medical Devices, Clinical Imaging and Bioinstrumentation.Dr. Bhavana Kotla, The Ohio State University Visiting Assistant Professor, Department of Engineering Education, College of Engineering, The Ohio State University ©American Society for Engineering Education, 2025 Assessing the Impact of Generative AI in Developing and Using Grading Rubrics for Engineering CoursesAbstractEngineering education is rapidly integrating generative artificial-intelligence (GenAI) tools thatpromise faster, more consistent assessment—yet their reliability in discipline-specific contextsremains uncertain. This mixed-methods study compared ChatGPT-4, Claude 3.5, and PerplexityAI across
[4]. In2024, more research is available for AI in education and industry, including as a virtual assistantusing AI as a prompting tool [14], and as a development bot to enhance software design [18].With the popularity of ChatGPT increasing, from one million users in the first week during thelaunch in 2022, to more than 200 million weekly users in late 2024 [16], the usage of ChatGPTin college engineering courses is expected to follow a significant increase soon. Using AI in the engineering classroom has been seen to offer both advantages anddisadvantages. Students saw an increased confidence in decision-making, critical thinking andproblem-solving skills using AI tools, which may help develop professional skills [14]; however,the
discussions in higher educationincluding its potential uses in and beyond the classroom. Initially, the focus was primarily onpreventing students from using generative AI tools, but attention is now shifting towardintegrating these tools into teaching and learning [1]. Many educators are exploring ways toincorporate generative AI into instruction [2].Students are often assumed to be tech-savvy [3]. With the widespread use of tools like ChatGPT,they may also be perceived as competent users of generative AI. However, effectively using AIfor learning requires more than just basic digital literacy, which can impact both the learningexperience and its benefit. Therefore, studying students’ interactions with AI is important, as thefindings will shape how
their writing in sustained or long-term writing projects[13, 14]. Due to thismodule, the majority of students were optimistic towards using AI in future assignments forwriting. However, students who use ChatGPT to write tend to run into common pitfalls such asambiguous writing, bias reinforcement, and “hallucinations”[15]. This shift reflects the need toprovide clear guidance on appropriate AI usage in educational settings. This work highlights thegrowing recognition that fostering AI literacy is a crucial educational practice in modernclassrooms.To investigate the ways students respond to AI literacy efforts and how they may change theiruse of genAI in these situations, we introduce structured usage of AI in one lecture to increase AIliteracy
withsports. These findings suggest the need for alternative analogies that better resonate with diversestudent backgrounds.For the solar charging station analogy, 72% of students matched all terms correctly, although someconfusion persisted. For example, 10% mistook ‘DC Source’ for the interface controller, and 15%confused ‘computer controller’ with the image of a cell phone. These findings suggest areas forrefining analogies, particularly in distinguishing components with similar terminology.A survey conducted at the end of the semester confirmed that students preferred real-worldanalogies over AI tools like ChatGPT, highlighting their value in establishing a strong conceptualfoundation and boosting confidence. Table 1 presents key survey results
. The Chronicle of Higher Education has observed, One year after its release, ChatGPT has pushed higher education into a liminal place. Colleges are still hammering out large-scale plans and policies governing how generative AI will be dealt with in operations, research, and academic programming. But professors have been forced more immediately to adapt their classrooms to its presence. Those adaptations vary significantly, depending on whether they see the technology as a tool that can aid learning or as a threat that inhibits it [11]. Faculty perspectives and responses are particularly critical in professional programs suchas engineering, medicine, and teacher preparation, where the rapid integration
and scipy (version 1.7.1) for statistical computations. IEEE Open Journal of the Communications Society, vol. 4, pp. 2952 2971, 2023, doi: 10.1109/OJCOMS.2023.3320646.tools such as Large Language models like ChatGPT are being The sentiment analysis component utilized multiple indicators A moderate positive correlation was observed between self-reported AI [3] Li Y, Wang C, Cao Y, et al. Human pose estimation based in-home lower body rehabilitation system[C]//2020implemented in educational institutions to provide personalized learning
disrupt inequities. Manywidely used AI tools, such as ChatGPT, are trained on massive proprietary datasets controlled byprivate corporations, raising questions about data security, bias, and accessibility. These concernsare particularly pressing in education, where AI’s role in student and faculty interactions must becritically examined. Without transparent and equitable governance, AI risks reinforcing existingpower imbalances rather than dismantling them.By centering only on the technical aspects of AI, we risk unintended consequences that reinforcesystemic inequities, creating outcomes that disproportionately harm marginalized groups. Someresearchers are exploring ethics, bias, and social responsibility regarding AI [5]. In this practicepaper
al.emphasized that utility value and self-efficacy are essential in shaping the learning outcomes ofengineering students, highlighting the importance of integrating practical applications into thecurriculum to enhance students' perceived utility value [3]. In the context of using large languagemodel (LLM) like ChatGPT in engineering education, the utility value can significantlyinfluence how students perceive and utilize these tools. Recent studies have explored the impactof LLM on students' perception of utility value in using AI tools. For instance, Rosenzweig et alexamined the effectiveness of utility value interventions in online math courses and found thatsuch interventions significantly increased students' perceived utility value and
of Blind and Visually Impaired Students and the Impact of Generative AI: A NarrativeAbstractThe advent of Generative AI (GenAI) in our society has taken root so deeply that simple Googlesearches invoke a GenAI response attempting to synthesize a simplified summary for a user.Incidentally, these GenAI systems like ChatGPT from OpenAI, LLaMA from Meta, Geminifrom Google, and Copilot from Microsoft are all largely text-based large language modelsproviding an increased level of access to people who use screen reading technology to interactwith personal computing systems. This study investigates the impact of GenAI on accessibilityfor blind and visually impaired students, focusing on the experiences of two computing
-dictive power on performance outcomes. Finally, we call for continued empirical research on theefficacy of LLM-based technologies in STEM education and propose future research directions inexploring their impact on teaching and learning.1 IntroductionThe introduction of OpenAI’s ChatGPT in November 2022 [1] triggered an unprecedented surgeof interest in applications of artificial intelligence (AI) based on Large Language Models (LLMs)and their underlying transformer architecture.In particular, LLMs appear to be exceptional in applications that involve human interaction, infor-mation retrieval, and summation, making them an attractive prospect for improving the effective-ness and accessibility of education in the digital age [2, 3, 4]. However
a growth in academic integrityfilings since the advent of ChatGPT. In fact, [2] points to a Stanford University survey where1/6th of students said they had used ChatGPT on assignments or exams. This article [2] alsopoints towards the issues of hallucinations, where AI focuses on generating text that sounds goodbut may not be scientifically accurate. However, [1] also points to potential efficiencies andutility of AI in higher education, such as teaching ethical use of AI, growth of tutoring/teachingassistants and for operational efficiencies. Auon [3] discussed the impact of AI on the humanexperience in physical (personalized medicine/drug delivery and disease identification),cognitive (increased workplace productivity, focused effort on
outputs of bothmodels.For alignment, fuzzy matching techniques were used. These techniques matched sentences be-tween GPT-4o and DeepSeek R1, even when there were minor differences in phrasing. This ap-proach improved the accuracy of mapping and ensured consistency in the processed data. Theresult was a clean and reliable dataset for analysis.5.2. Overall Categorization CoverageWe analyzed the total number of sentences processed and the extent to which GPT-4o and DeepSeekR1 provided category assignments. Table 1 summarizes the categorization coverage across all an-alyzed sentences. ChatGPT DeepSeek Total Sentences 1823 1823
hypotheses for future research.A preliminary qualitative analysis of Ticket Home student exit surveys and TA Ticket Homesummaries used ChatGPT to identify common themes. The surveys were first manuallyanonymized by removing names, then entered into ChatGPT with the prompt “Please summarizethese responses to the question: . List the most common themes and how manyresponses mentioned each of them.” While a similar approach has been used successfully forthematic analysis before [20], our approach involves different data and prompts; it shouldtherefore be considered preliminary and subject to a more extensive validation. To assess theapproximate accuracy of this approach, a human rater manually identified the four most commoncodes identified by ChatGPT
-confidence in their individualskills in oral communication, specifically related to presentations, but these results requireadditional research to confirm these findings.Using AI to Assess Student Outcomes: Co-Pilot and ChatGPT were both used to evaluate pitchtranscripts using the Grading Rubric. The results of AI-evaluations were compared to the facultyevaluations using the same grading rubric. This was limited to transcripts of the pitch as anyonline video-based platform for video analysis was by paid subscription only. In identifyingavailable AI tools, some interesting subscription-based options we discovered. These tools focusspecifically on video analysis of body language and pitch performance, including uSpeek(Sarang, 2023) and Bodha (Cadet
prompt to AI. Thus, a lack of effective communications skills can compromise thequality of the generated output if the question is not clearly formulated, and the prompts are notrefined or elaborated. Moreover, without an expert to evaluate the generated solution, there is adanger that the solution is based on incorrect or biased information [16]. Unless the decisionmakers are able to critically evaluate the generated solutions, they may make costly mistakes.Farrokhnia, Banihashem, Noroozi, and Wals [17] completed a SWOT analysis of ChatGPT – agenerative AI tool which is commonly used in higher education by instructors and students. Theyidentified the following weaknesses and threats of generative AI: • Lack of deep understanding of the
], rapidimprovement in the performance of these products, as reflected by one faculty’s experience ofPerplexity AI scoring 80% on their multiple choice-based engineering quiz, accentuated the needfor BME educators and students to improve AI literacy and cultivate responsible use of AI.ML algorithms are computer programs that improve their performance with more experience(data) [8]. Therefore, problems in the data used to train ML algorithms, such as demographicbiases, can be reflected in the performance of ML algorithms. In a BME context, GPT-4, whichpowers ChatGPT and Perplexity AI, showed strong ethnic biases when assigning medicalconditions such as HIV/AIDS [9], while GPT-4 and Gemini (also powers AI-enabled notebook,NotebookLM) showed negative perception
using ChatGPT for high-level analysis. Datasets weremanually formatted to ensure consistent wrangling by the AI, using standardized key phrases andstructured formatting to enhance the AI's ability to parse and interpret the information accurately. Thisstudy implemented a simplistic segmentation, considering each sentence as a single statement, toimprove reliability and repeatability. This process was systematically repeated and refined by utilizingsubsets of the data with established qualities until preprocessing consistently achieved accurate parsingfollowing emerging best practices [23].Throughout the analysis, refinements were made to prompts and categorizations, ensuring alignmentwith the nuances of each reflection. Reanalysis occurred in
ChatGPT [1], Google’s Gemini [2],and Microsoft’s Copilot [3] have gained widespread student adoption due to their free access andease of use. This expansion has occurred amid varying acceptance [4–6] and trust [7] in digitallearning technologies across student populations through the COVID-19 pandemic and into thepresent day. Approximately one-third (35.4%) of students reported regular usage of ChatGPT,while 47% expressed concern about AI’s impact in education [8]. Additionally, 60% reported thattheir instructors or schools had not yet provided guidelines for ethical or responsible AI tool use [8].As students increasingly use available online AI assistants, researchers have concurrently devel-oped specialized educational AI tools designed