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1. Insights from the First Year of Project # 2044472 “Improving the Conceptual Mastery of Engineering Students in High Enrollment Engineering Courses through Oral Exams

   Qi, H.; Lubarda M.; Schurgers C.; Sandoval C.; Klement, L.; Ghazinejad M.; Relaford-Doyle, J.; Kim, M.; Minnes, M.; Baghdadchi S.; et al (June 2022, ASEE Annual Conference proceedings)

   This project aims to enhance students’ learning in foundational engineering courses through oral exams based on the research conducted at the University of California San Diego. The adaptive dialogic nature of oral exams provides instructors an opportunity to better understand students’ thought processes, thus holding promise for improving both assessments of conceptual mastery and students’ learning attitudes and strategies. However, the issues of oral exam reliability, validity, and scalability have not been fully addressed. As with any assessment format, careful design is needed to maximize the benefits of oral exams to student learning and minimize the potential concerns. Compared to traditional written exams, oral exams have a unique design space, which involves a large range of parameters, including the type of oral assessment questions, grading criteria, how oral exams are administered, how questions are communicated and presented to the students, how feedback were provided, and other logistical perspectives such as weight of oral exam in overall course grade, frequency of oral assessment, etc. In order to address the scalability for high enrollment classes, key elements of the project are the involvement of the entire instructional team (instructors and teaching assistants). Thus the project will create a new training program to prepare faculty and teaching assistants to administer oral exams that include considerations of issues such as bias and students with disabilities. The purpose of this study is to create a framework to integrate oral exams in core undergraduate engineering courses, complementing existing assessment strategies by (1) creating a guideline to optimize the oral exam design parameters for the best students learning outcomes; and (2) Create a new training program to prepare faculty and teaching assistants to administer oral exams. The project will implement an iterative design strategy using an evidence-based approach of evaluation. The effectiveness of the oral exams will be evaluated by tracking student improvements on conceptual questions across consecutive oral exams in a single course, as well as across other courses. Since its start in January 2021, the project is well underway. In this poster, we will present a summary of the results from year 1: (1) exploration of the oral exam design parameters, and its impact in students’ engagement and perception of oral exams towards learning; (2) the effectiveness of the newly developed instructor and teaching assistants training programs (3) The development of the evaluation instruments to gauge the project success; (4) instructors and teaching assistants experience and perceptions. 

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2. Evaluating Oral Exams in Large Undergraduate Engineering Courses

   Schurgers C.; Baghdadchi S.; Lubarda M.; Ghazinejad M.; Phan A.; Qi H. (December 2021, IEEE-AP-S URSI 2021)

   While studies have shown that oral exams are a valuable method of assessment, their use has been limited due to concerns about scalability, examiner bias, and student anxiety. This paper presents preliminary results on incorporating oral exams into two large undergraduate engineering courses, examining the potential viability of these assessment strategies. This work was done when the courses were offered remotely due to COVID-19, but the results offer valuable insights that could carry over to in-person instruction as well. 

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3. Education in a Remote World: Focus on Workforce Readiness

   Delahanty, C.; Genis, V.; Herring, S.; & Timby, T.A (July 2021, 2021 ASEE Virtual Annual Conference Content Access)

   Bucks County Community College (Bucks) in collaboration with Drexel University (Drexel) is committed to increasing the number of workforce ready engineers and engineering technicians and to creating a blueprint for 2+2 engineering education programs nationally. Recently, educational reform took an unexpected turn to remote teaching due to the world-wide COVID-19 pandemic. Within our NSF ATE grant to enhance our present engineering technology curriculum we modified and enhanced instructional and student engagement methods to assure workforce readiness of our students in a remote world. Curriculum enhancements within the engineering technology (ET) occupational major at Bucks and the B.S. in ET degree program at Drexel, modifications to delivery of workforce development certification programs through the Bucks Center for Workforce Development (CWD), and college-wide student engagement strategies were implemented to assure quality education and student engagement. Modifications to credit courses included asynchronous online courses, synchronous remote courses, and hybrid courses, which combined remote and on campus laboratory instruction. Our CWD implemented hybrid instruction that included necessary resources for students such as tool kits and borrowed laptop computers. In addition, a college wide program called Bucks+ was implemented through the Bucks Business and Innovation Department to increase enrollment, retention, and workforce readiness of students. The Bucks+ program focuses on student engagement through competition within curriculum, and extracurricular endeavors that prepare students for industry. We will share our successes and challenges within our call to action to engage students in a remote world and to enhance their educational experience through innovative instructional techniques. 

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4. Maximizing Academic Integrity While Minimizing Stress in the Virtual Classroom

   https://doi.org/10.1128/jmbe.00292-21  

   Novick, Peter A.; Lee, Jacqueline; Wei, Sujun; Mundorff, Emily C.; Santangelo, Jessica R.; Sonbuchner, Timothy M. (April 2022, Journal of Microbiology & Biology Education)

   ABSTRACT The article documents students’ experiences with the shift online at the onset of the COVID-19 pandemic and provides informed recommendations to STEM instructors regarding academic integrity and student stress. Over 500 students were surveyed on these topics, including an open-ended question. Students experienced more stress and perceived a greater workload in online courses and therefore preferred in-person courses overall. Personal awareness of cheating during online exams is positively correlated with the proportion of cheating a student perceives. Fear of getting caught is the best cheating deterrent while getting a better grade makes cheating most enticing. Randomization of questions and answer choices is perceived as a highly effective tool to reduce cheating and is reported as the least stress-inducing method. Inability to backtrack and time limits cause students the most stress. Students report that multiple choice questions are the least effective question type to discourage cheating and oral exam questions cause the most stress. Use of camera and lockdown browser or being video- and audio- recorded caused the majority of student stress. Yet, nearly 60% agree that the combination of camera and lockdown browser is an effective deterrent. Recommendations: (i) Be transparent regarding academic dishonesty detection methods and penalties. (ii) Use online invigilating tools. (iii) Synchronize exams and (iv) randomize exam questions. (v) Allow backtracking. (vi) Avoid converting in-person exams to online exams; instead, explore new ways of designing exams for the online environment. 

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5. Integrating Artificial Intelligence Across the Engineering Curriculum: Student Perceptions, Trust, and Ethical Awareness from Two Institutions

   Miar, S; Motaref, S (July 2026, ASEE)

   Artificial intelligence (AI) platforms are increasingly used by engineering undergraduates for problem solving, technical writing, visualization, and conceptual scaffolding. However, many university policies still frame AI usage within the context of plagiarism, limiting its intentional integration into coursework. In engineering education, unmonitored AI use (e.g., generating homework or project solutions), without verifying accuracy, can undermine learning objectives and assessment validity. At the same time, engineering practice increasingly demands AI literacy and prompt-engineering skills, positioning responsible AI use as a core competency for future engineers This study investigates structured AI integration across four engineering courses spanning the freshman, sophomore, junior, and senior levels at two institutions in the northeastern United States: The University of Connecticut (UConn), a public research university with large classrooms, and the University of Hartford (UHart), a private primarily undergraduate institution with smaller class sizes. The courses examined in this study were those taught by the authors during the Fall 2025 semester, enabling deliberate integration of AI activities across a range of engineering disciplines, academic levels, and instructional formats. At the public university, the study was implemented in two courses: Mechanics of Materials (enrollment ≈120 students) and Design of Reinforced Concrete Structures (enrollment ≈50 students). In the private undergraduate institution, AI was integrated into classes at the freshman-level Engineering and Design with 19 students from different engineering disciplines and senior-level Biomedical Engineering Capstone Design with 12 students. Students were encouraged to apply AI tools to diverse learning tasks, such as problem-solving, visualization, study-guide creation, and capstone project support. The instructional design emphasized prompt design, critical verification of AI outputs, and metacognitive reflection on the reliability and educational value of AI-generated content. To evaluate the impact, students completed reflection assignments assessing AI capabilities and deficiencies, as well as an anonymous post-intervention survey measuring perceptions of learning, confidence in evaluating AI results, and awareness of ethical implications. The analysis will examine (a) whether academic level influences students’ perceptions of AI integration, and (b) whether institutional type (public vs. private) affects students’ attitudes and reported benefits. Preliminary results suggest that guided AI use can enhance engagement, foster self-monitoring skills, and promote a balanced understanding of both the potential and limitations of generative AI in engineering education. This work contributes to the growing body of research on AI-inclusive pedagogy and offers a scalable framework for aligning AI integration with academic integrity and ABET-aligned learning outcomes. 

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