2026 ASEE Annual Conference & Exposition

Teaching Computational Intelligence Course by a Spiral Curriculum and CDIO-based Hybrid Pedagogical Approach

Presented at Electrical and Computer Engineering Division (ECE) Technical Session 18

This paper will adhere strictly to the guidelines and submission deadlines of the Electrical and Computer Engineering (ECE) Division of ASEE for full paper submission. Following the acceptance of this abstract if any, the full draft of the paper will be prepared and submitted by January 21, 2026. The revised full paper will be submitted by February 18, 2026, and the final version will be completed and submitted by April 29, 2026.

This paper presents the design and implementation of a Computational Intelligence (CI) course in Department of Electrical and Computer Engineering (ECE) using a hybrid pedagogical approach that integrates the Conceive-Design-Implement-Operate (CDIO) framework with a spiral curriculum (SC) model. The goal is to help ECE students develop a balanced mastery of theoretical foundations and practical applications in computational intelligence, encompassing evolutionary computation, swarm intelligence, bio-inspired neural networks, fuzzy systems, and nature-inspired optimization.

In this course, students work in teams to address real-world CI challenges, progressing through the CDIO cycle with project milestones aligned to course topics. The CDIO framework provides the structural backbone for project-based learning, while the spiral curriculum reinforces conceptual continuity revisiting previously taught material at increasing levels of complexity. Originally introduced by Jerome Bruner (The Process of Education, 1960), the spiral approach enables students to connect new learning to prior knowledge, deepening comprehension and retention. The resulting adaptive CDIO-SC method promotes cyclic reinforcement, strengthens conceptual integration, and supports cumulative learning across multiple chapters and modules.

The course is organized around the four CDIO stages:
• Conceive: Students identify computational intelligence challenges such as optimization under uncertainty, adaptive decision-making, or autonomous learning in complex environments. They define project objectives, analyze problem features, and explore industrial, societal, and research applications.
• Design: Students develop algorithmic models using evolutionary computation, swarm intelligence, fuzzy systems, or bio-inspired neural networks. Emphasis is placed on mathematical modeling, parameter analysis, and balancing exploration versus exploitation, accuracy versus computational efficiency, and adaptability versus convergence.
• Implement: Students translate their designs into simulation environments (e.g., MATLAB, Python) to develop metaheuristic algorithms and hybrid models such as neuro-fuzzy or genetic-fuzzy systems. Through implementation, they gain hands-on experience transforming theoretical formulations into functional computational tools.
• Operate: Students evaluate their algorithms using benchmark datasets or real-world optimization and control problems. They assess robustness, convergence rate, generalization ability, and computational cost, refining their designs through iterative testing and analysis.

Representative projects include fuzzy-genetic optimization for nonlinear control systems, swarm-based path planning for multi-agent coordination, self-organizing neural networks for pattern recognition, and evolutionary optimization for feature selection and parameter tuning. Learning outcomes are assessed through algorithm performance analysis, simulation studies, technical reports, presentations, and reflective learning journals. Evaluation metrics address not only technical correctness and innovation but also teamwork, communication, and the integration of theory with practice.

Preliminary results show that students demonstrate enhanced competencies in CI algorithm design, system integration, and problem-solving. They are increasingly capable of translating mathematical and computational principles into engineering solutions for robotics, control, and embedded systems applications. Evidence from assignments, Q&A sessions, examinations, ABET-style assessments, self-assessment surveys, and formal course evaluations compared to results from previous years indicates that the hybrid CDIO-SC pedagogy significantly improves engagement, conceptual retention, and hands-on learning outcomes. This work demonstrates that integrating CDIO with a spiral curriculum provides an effective and scalable instructional model for computational intelligence education. The approach develops both technical expertise and professional competencies, including critical thinking, innovation, teamwork, and system-level reasoning skills essential for modern engineers who integrate AI and computational intelligence to solve interdisciplinary challenges. Future work will expand longitudinal ABET assessments, foster interdisciplinary collaboration, and disseminate modular CI project materials for broader adoption across engineering curricula.

Authors

Dr. Tingjun Lei http://orcid.org/0000-0002-9043-5600 University of North Dakota [biography]

Dr. Tingjun Lei is currently an Assistant Professor in the School of Electrical Engineering and Computer Science at the University of North Dakota. He received his Ph.D. degree in electrical and computer engineering with the Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA., in 2023, his M.S. degree in electrical and computer engineering from the New York Institute of Technology, Old Westbury, NY, USA, in 2016, and the B.S. degree in transportation engineering from Shanghai Maritime University, Shanghai, China, in 2014. He developed and taught the CSCI 242 Algorithms and Data Structure, EE 457 Robotics Fundamentals, EECS 590 Data Science in Robotics and EE 752 Introduction to Autonomous Systems at UND, providing hands-on experience and mentoring to undergraduate and graduate students, fostering a deep understanding of basic algorithms, data analysis, autonomous robot systems and applications. Dr. Lei received the Best Paper Awards from Biomimetic Intelligence and Robotics (Elsevier), 2025 Cyber Awareness and Research Symposium 2026 (CARS 2025), 2022 International Conference on Swarm Intelligence (ICSI 2022) and the 2024 American Society for Engineering Education (ASEE ECE Division). He also received Best of Track Paper Award from Integrated Communications Navigation and Surveillance (ICNS 2026) conference, and Honorable Mentions Awards from the International Joint Conference on Neural Networks (IJCNN 2025), IEEE International Conference on Robotics and Biomimetics (IEEE ROBIO 2025), IEEE International Midwest Symposium on Circuits and Systems (IEEE MWSCAS 2025). Dr. Lei serves as an Editorial Board Member for Intelligence and Robotics, Robot Learning, IET Cyber-Systems and Robotics, Designs, and Biomimetic Intelligence and Robotics. He has also actively contributed as a Technical Program Committee (TPC) member for numerous international conferences, including IEEE CEC, IEEE IJCNN, ICSI, and PRIS. He was awarded the Early Career Scholars Award by the University of North Dakota. His two papers have been selected and featured as cover articles on Intelligence and Robotics. His research spans multiple interdisciplinary areas, including bio-inspired artificial intelligence (AI), robotics and autonomous systems, optimization and evolutionary computation, human-autonomy teaming (HAT), intelligent transportation systems, and applied machine learning.
Timothy Sellers http://orcid.org/0000-0001-8344-9804 Mississippi State University [biography]

Timothy Sellers (Student Member, IEEE) received the B.S. degree in Robotics and Automation Technology and Applied Science in Electro-Mechanical Engineering from the Alcorn State University, Lorman, MS, USA in 2020. He is currently pursuing a Ph.D. degree in the Department of Electrical and Computer Engineering at Mississippi State University, Mississippi State, MS, USA. His research interests include robotics, autonomous systems, navigation, nature-inspired intelligence, and machine learning.
Dr. Chaomin Luo http://orcid.org/0000-0002-7578-3631 Mississippi State University [biography]

Dr. Chaomin Luo received his Ph.D. degree in electrical and computer engineering from the Department of Electrical and Computer Engineering at the University of Waterloo, Canada. He also earned an M.Sc. degree in engineering systems and computing from the University of Guelph, Canada, and a B.Sc. in electrical engineering from Southeast University. Currently, he is a Professor in the Department of Electrical and Computer Engineering at Mississippi State University. His research interests include engineering education, robotics, control, automation, and robotics education. He is Associate Editor in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). He is Tutorials Co-Chair in the 2020 IEEE Symposium Series on Computational Intelligence. Dr. Luo was the recipient of the Best Paper Awards in ASEE2024, Biomimetic Intelligence and Robotics (Journal) 2024, IEEE International Conference on Information and Automation, International Conference on Swarm Intelligence, and SWORD Conference. Dr. Luo is active nationally and internationally in his research field. He was the Program Co-Chair in 2018 IEEE International Conference on Information and Automation (IEEE-ICIA’2018). He was the Plenary Session Co-Chair in the 2021 and 2019 International Conference on Swarm Intelligence, and he was the Invited Session Co-Chair in the 2017 International Conference on Swarm Intelligence. He was the General Co-Chair of the 1st IEEE International Workshop on Computational Intelligence in Smart Technologies (IEEE-CIST 2015), and Journal Special Issues Chair, IEEE 2016 International Conference on Smart Technologies (IEEE-SmarTech), Cleveland, OH, USA. He was Chair and Vice Chair of IEEE SEM - Computational Intelligence Chapter and was a Chair of IEEE SEM - Computational Intelligence Chapter and Chair of Education Committee of IEEE SEM. He has organized and chaired several special sessions on topics of Intelligent Vehicle Systems and Bio-inspired Intelligence in reputed international conferences such as IJCNN, IEEE-SSCI, IEEE-CEC, IEEE-CASE, and IEEE-Fuzzy, etc. He has extensively published in reputed journals and conference proceedings, such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on SMC, IEEE Transactions on Cybernetics, IEEE-ICRA, and IEEE-IROS, etc. Dr. Luo serves as Associate Editor of IEEE Transactions on Cognitive and Developmental Systems, International Journal of Robotics and Automation, Biomimetic Intelligence and Robotics, and Intelligence and Robotics.
Prof. Zhuming Bi P.E. http://orcid.org/0000-0002-8145-7883 Purdue University Fort Wayne [biography]

Zhuming Bi (Senior Member, IEEE) received the Ph.D. degree from the Harbin Institute of Technology, Harbin, China, in 1994, and the Ph.D. degree from the University of Saskatchewan, Saskatoon, SK, Canada, in 2002. He has international work experience in Mainland China, Hong Kong, Singapore, Canada, UK, Finland, and USA. He is currently a professor of Mechanical Engineering with Purdue University Fort Wayne, Fort Wayne, IN, USA. His current research interests include robotics, mechatronics, Internet of Things (IoT), digital manufacturing, automatic robotic processing, and enterprise information systems. He has published 6 research books and over 180 journal publications in these fields.
Dr. Umar Iqbal http://orcid.org/0000-0001-7683-5045 Illinois State University [biography]

Dr. Iqbal is an Associate Professor at Electrical Engineering, Illinois State University. He worked in the areas of Multi-Sensor Integration, Navigation Systems, Robotics, Control, and Educational Research. Dr. Iqbal has designed and taught over 25 courses, spanning topics from robotics and control systems to circuits and design. His research, including blended learning and COVID-19’s impact, shapes modern engineering education.

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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