2026 ASEE Annual Conference & Exposition

Quantum AI for Engineering Education: Assessment of Students’ Problem-Solving Performance in Electric Circuits

Presented at Computers in Education (CoED): Best of CoED Paper Session - Division Special Events (2 of 4) -- M508D

Quantum computing marks a transformative shift in information processing by leveraging the fundamental principles of quantum mechanics—entanglement, superposition, and interference—to achieve computational capabilities beyond those of classical systems. As Noisy Intermediate-Scale Quantum (NISQ) devices continue to mature, researchers are actively developing quantum algorithms and exploring their applications across a range of disciplines. Prior studies highlighted mathematical parallels between Support Vector Machine (SVM) and quantum classifier, suggesting that quantum model may offer comparable classification performance.
This exploratory study investigates the effectiveness of a Variational Quantum Classifier (VQC) in evaluating student performance data within engineering education, specifically in problem-solving contexts. The dataset comprises 363 solution events collected from a “Fundamental Electronics for Engineers” course at a mid-sized Carnegie R1 university in the western United States. These events are categorized into three cognitive task types based on the PPST framework. A hybrid SMOTEN resampling method was applied to further increase the sample size for this study setting.
Each student response was evaluated by two independent raters using Docktor et al.'s five process-oriented constructs adapted from their rubric: Useful Description, Engineering Approach, Specific Application of Engineering Principles, Mathematical Procedures, and Logical Progression. Based on expert judgment, responses were classified into three performance levels or classes: High, Medium, and Low.
Quantum machine learning models were developed and tested using IBM’s gate-based quantum computing simulator. Multiple configurations were explored, including variations in encoding circuits, Ansatz structures, circuit depths, and optimization algorithms such as ADAM, COBYLA, TNC, and SLSQP. Model performance was assessed using standard evaluation metrics, including accuracy, precision, recall, F1-score, and specificity. Results from the simulated environments were analyzed and compared in this study.
The findings offer preliminary insights into the feasibility of quantum machine learning (QML) for educational assessment, and point toward promising directions for the integration of quantum AI-driven tools in engineering education for the future.

Authors

Mr. Rifatul Islam Himel http://orcid.org/0000-0002-0415-4774 Utah State University [biography]

Rifatul Islam Himel is a doctoral candidate in Engineering Education at Utah State University (USU), Logan, Utah, US. His doctoral research focuses on understanding human cognition in engineering problem-solving. He holds a Bachelor's degree in Electrical and Electronics Engineering and a Master's degree in Electronics and Telecommunication Engineering. With over six years of professional experience in the Information Technology industry and a strong background in teaching and research, Rifatul brings a unique interdisciplinary perspective to his work. His research interests include Human and Machine Cognition, Applied Artificial Intelligence in Engineering Education, Information Processing, and the study of Consciousness.
Dr. Oenardi Lawanto Utah State University [biography]

Dr. Oenardi Lawanto is a professor in the Department of Engineering Education at Utah State University, USA. He received his B.S.E.E. from Iowa State University, his M.S.E.E. from the University of Dayton, and his Ph.D. from the University of Illinois at Urbana-Champaign. Dr. Lawanto has a combination of expertise in engineering and education and has more than 30 and 14 years of experience teaching engineering and cognitive-related topics courses for his doctoral students, respectively. He also has extensive experience in working collaboratively with several universities in Asia, the World Bank Institute, and USAID to design and conduct workshops promoting active-learning and life-long learning that is sustainable and scalable. Dr. Lawanto’s research interests include cognition, learning, and instruction, and online learning.
Dr. Yashin Brijmohan http://orcid.org/0000-0003-2485-9808 Utah State University [biography]

Yashin Brijmohan is a registered professional engineer who is currently appointed as Chairman of Engineering Education Standing Technical Committee of the Federation of African Engineering Organizations, Executive committee member of the Commonwealth Engineers Council, Board Member of the UNESCO International Centre for Engineering Education, and Co-Chair of the Africa Asia Pacific Engineering Council.

He was the founding Executve Dean of Business, Engineering and Technology at Monash South Africa, former Vice President of the World Federation of Engineering Organizations, and led several committees in the engineering profession.

Yashin has both leadership and specialist experience within the engineering power industry and education sectors and is known for his thought leadership in capacity building and engineering education.
Dr. Wade H Goodridge http://orcid.org/0000-0002-5811-7629 Utah State University [biography]

Wade Goodridge is a tenured Associate Professor in the Department of Engineering Education at Utah State University. His research often has a focus on what drives us to learn, how we learn and how well we do it, and what can improve that learning. This work is often directed with a spatial thinking emphasis. Dr. Goodridge has investigated different learning modalities (fully online, hybrid, blended courses), cognitive and metacognitive learning processes (self-regulation, engineering problems solving, neural efficiency in engineering problem solving) learning motivators (engineering self-efficacy), and effective and personalized engineering education interventions (tactile engineering curriculum, physical teaching models, XR environments and AI informed systems of delivering and monitoring engineering learning). Dr. Goodridge has created a valid and reliable instrument to assess spatial ability in blind and low vision (BLV) learners thus opening new areas of research centered on tactile spatial ability. He has created tactile and visual learning tools and has offered professional development experiences on how to improve the education of tomorrows engineers.
Dr. Zain ul Abideen Utah State University [biography]

Zain ul Abideen holds a Ph.D. in Engineering Education from Utah State University, where he also served as a Graduate Researcher. He is a computer engineer and earned his bachelor’s degree in computer engineering and master’s degree in engineering, and brings over a decade of experience in teaching and research with undergraduate engineering students. He actively contributes to several technical committees and serves as a reviewer for leading journals and international conferences. His doctoral research focused on cognitive engagement, AI in education and engineering design, self-regulated learning, and mobile learning. His work seeks to deepen our understanding of how these elements influence learning and success in engineering education. With a strong commitment to educational advancement and a robust academic background, Zain is recognized as a dedicated and emerging scholar in the field of engineering education.
Sehrish Jabeen Utah State University [biography]

Sehrish Jabeen is currently a Ph.D. student and Graduate Research/Teaching Assistant. She brings over five years of teaching experience across K–12 and undergraduate educational settings, along with seven years of professional experience in the field of information technology, where she worked in the banking sector. Her research interests include mathematical well-being, mathematical modeling and problem-solving, and the application of Cognitively Guided Instruction (CGI) in mathematics and engineering education. Through her work, Sehrish aims to explore how instructional practices and learning environments can support students' mathematical thinking and their engagement with engineering and mathematics problem-solving.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 21, 2026, and to all visitors after the conference ends on June 24, 2026

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