The integration of artificial intelligence (AI) into undergraduate engineering education is increasingly critical for preparing students for the evolving demands of the workforce. However, universities face challenges in effectively embedding AI concepts into interdisciplinary curricula. This work-in-progress study analyzes AI-infused undergraduate engineering programs at four Canadian universities, focusing on curriculum structure, learning outcomes, and student engagement with AI concepts.
Using the TASKS framework (Task, Affect, Skills, Knowledge, Stress), this study examines how AI is introduced within core and elective engineering courses, evaluating the extent to which students develop technical proficiency, problem-solving abilities, and adaptability to AI-driven technologies. The framework aims to enhance students' technical competencies while ensuring alignment with the Canadian Engineering Accreditation Board (CEAB) standards. In addition, the study highlights institutional efforts in AI integration, including faculty development initiatives and support structures that facilitate student learning.
Findings from this study contribute to the broader discussion on best practices for AI education in engineering, offering insights into curriculum design, accreditation considerations, and the student experience. By identifying gaps and opportunities in AI curriculum implementation, this research provides actionable recommendations to enhance AI literacy and workforce readiness among engineering graduates towards Industry 5.0.

Authored by
Mr. Md Sakib Ullah Sourav (Concordia University), Dr. Yong Zeng (Concordia University), Dr. Hua Ge (Concordia University), and Dr. Ali Akgunduz (Concordia University)

With the recent boom in artificial intelligence (AI) , AI has been added to countless tools to aid in our lives. As educators, we are interested in the possibility of applying recent developments of Generative AI models, particularly Large Language Models (LLMs), to help engineering students with their course work.

In this project, our goal is to get an AI program to act like a good Teaching Assistant (TA), providing a useful learning aid for students who have questions outside of office hours or for students who are not comfortable in office hours. This idea can perhaps also help where the TA budget is tight. Also, it might provide assistance for working engineers who are trying to learn on their own.

After proper training, the AI program ideally should answer correctly any class-related questions from a student. In our work, we are using course material for an undergraduate electrical-engineering course on transistor circuits. We have focused on early material on diode and transistor construction and operation, but we also tested the AI TA on some text-book chapters that cover single-transistor amplifiers.

In the paper, we will describe our approach to evaluating on-the-market models for this purpose, as well as, the training and fine-tuning of models to achieve a high-performance AI TA. We will present our evaluations of an AI TA’s ability to precisely point a student to sections in the given textbook as well as its ability to give a detailed explanation to a question, as a human TA would do. Limitations of the AI TA and potential extensions of this work will also be covered.

Authored by
Mr. Ernest Wang (University of California, Davis), Harry Zhang (University of California, Davis), Prof. Paul J. Hurst (University of California, Davis), Dr. Yubei Chen (University of California, Davis), and Kenneth Dyer (Microsoft Corporation)

Mussels play a crucial role in monitoring and reducing water pollution. As natural bio-indicators, they filter pollutants such as heavy metals, providing a sustainable method for water decontamination. This project leverages AI to monitor mussel behavior, particularly their gaping activity, to enhance the water purification process. We aim to employ advanced 3D reconstruction techniques to create detailed models of mussels, improving the accuracy of AI-based analysis. Specifically, we use a state-of-the-art 3D reconstruction tool, Neural Radiance Fields (NeRF), to create 3D models for analyzing mussel configurations and behavioral patterns. NeRF enables 3D reconstruction of scenes and objects from a sparse set of 2D images. To capture these images, we develop a data collection system capable of photographing mussels from multiple viewpoints. The system featured a turntable made of foam board, marker around the edges, with a designated space in the center for the mussels. The turntable was attached to a servo motor controlled by an ESP32 microcontroller. It rotated in 10-degree increments, with the ESP32 camera capturing an image at each step. The images, along with degree information and timestamps, are stored on an SD card. Several components, such as the camera holder and turntable base, are 3D printed. These images are used to train a NeRF model using the Python-based NerfStudio framework, and the resulting 3D models are viewed via the NerfStudio API. The setup is designed to be user-friendly, making it easy for students, including K-12-aged students, to create 3D reconstructions of their chosen objects. Both the embedded hardware and software are simple to build and implement. In the summer of 2024, a team of high school students from the Juntos Academy at NC State worked on this platform, gaining hands-on experience in embedded hardware development, basic machine learning principles, and 3D reconstruction from 2D images.

Authored by
Mr. Mayur Sanap (North Carolina State University at Raleigh), Arman Badalamenti (North Carolina State University at Raleigh), Devadharshini Ayyappan (North Carolina State University at Raleigh), Sanjana Banerjee (North Carolina State University at Raleigh), Mrs. Diana Milena Urieta (North Carolina State University at Raleigh), Dr. Caren Cooper (North Carolina State University at Raleigh), Prof. Michael Daniele (North Carolina State University at Raleigh), Dr. James Reynolds (North Carolina State University at Raleigh), Jay F Levine (North Carolina State University at Raleigh), Prof. Alper Bozkurt (North Carolina State University), and Dr. Edgar Lobaton (North Carolina State University)

This paper presents a hands-on outreach activity aimed at increasing high school students’ interest in engineering disciplines through a practical, real-world application: coffee brewing analysis and sensor interfacing. The activity, designed for 10th-grade students, introduces basic concepts in engineering, computing, and data analysis by allowing students to collect data using Phidget sensors and analyze coffee brewing variables such as pH, turbidity, and extraction yield. By combining curiosity-driven inquiry with interactive, hands-on learning experiences, this activity encourages students to explore potential careers in science, technology, engineering, and mathematics (STEM). We discuss the design of the lab, its implementation, and preliminary results from student feedback. Our findings suggest that this multifaceted, real-world application of engineering principles significantly enhances student engagement and understanding of engineering concepts.

Authored by
Dr. Kumar Yelamarthi (Tennessee Technological University), Dr. Susmit Shannigrahi (Tennessee Technological University), and Sahaya Jestus Lazer (Tennessee Technological University)

Signal integrity (SI) has become as one of the vital areas for the development of modern hardware systems that require working with digital signals at very high speeds. As it is also known, the federal government has budgeted about $280 billion in new funding for domestic research and manufacturing of semiconductors through the Creating Helpful Incentives to Produce Semiconductors (CHIPS) act. These new semiconductor chips are tightly packed with signal interconnects densely integrated in small spaces where there exist several coupling mechanisms leading to signal integrity problems. Furthermore, signal integrity requires the synergy of electrical and mechanical engineering, physics and another associated disciplines. However, nationally, there is still a need for engineers and scientists with SI skills. For example, it has been pointed out that: “The gap between the demand in the industry and the supply of engineers with signal integrity design skills is widening.” Additionally, the XX metropolitan area is known as having one of the highest concentrations of connector companies, where SI knowledge is needed.

In a previous paper, we reported that one of the problems with valuable signal integrity educational experiences is the specialized and costly equipment and software that are needed. For example, high-speed sampling oscilloscopes, bit error rate testers and up-to-date precision network analyzers, are very expensive and beyond the standard laboratory equipment in an undergraduate program. In this paper, we report the efforts that we have made to keep our signal integrity laboratory current and the new laboratory experiences, as well as capstone projects and undergraduate research, that we have supervised. For example, some projects have been How to Conduct a 4-port Mechanical Calibration on a 67 GHz vector network analyzer, Using Advanced Design System for a Detailed Examination of the Worst-case Impact of Vias on the Signal Integrity of a Signal when Traversing a Printed Circuit Board with 5 and 9 Layers and Obtaining Dielectric Characteristics of a Material Using the Free Space Measurement Method. In addition, we supervised a capstone project of Electromagnetic Compatibility and Electromagnetic Interference Evaluation Board, which was sponsored by an industry partner and is currently in use at the sponsor’s facility. Recently, we have obtained support from the Office Naval Research to acquire new equipment to continue our enhancement of our undergraduate/graduate education and research in the signal integrity field.

Furthermore, we can proudly state that we have graduated over sixty students who have taken a course on signal integrity or have done internships in the signal integrity laboratory and are now working in the SI field worldwide. Furthermore, students highly rated the course (6/7) and provided comments such as “Lectures and hands-on during labs. The use of many SI equipment and simulation tools plays a big part on understanding the materials.” We are also planning to contribute to the CHIPS act by partnering with our sister campus that obtained CHIPS funding and develop a skilled and diverse pipeline of workers for the national semiconductor industry and its associated industries, such as connectors and PCB manufacturing.

The Authors thank the Office of Naval Research, Award #: xxxx, for their support.

Authored by
Dr. Aldo Morales (Pennsylvania State University, Harrisburg, The Capital College) and Dr. Sedig Salem Agili (Pennsylvania State University, Harrisburg, The Capital College)

Underrepresented sophomore and junior engineering students come to the classroom with misconceptions or rumors about the first course in Electric Circuits. Despite passing their Calculus II and Physics I courses, some of them have little to no understanding of concepts such as voltage, current or power. This lack of knowledge leads to misconceptions, learning difficulties and low test scores. A significant number of students fear taking Electric Circuits. When surveyed at the beginning of the semester, 38 percent of our 46-student class stated they were nervous about taking Electric Circuits. Solving circuit problems correctly is difficult for most students. One student said, “Unlike our calculus courses, these questions to evaluate voltage and current are word problems - they are puzzles like I have never seen before.”
Only 63 percent of students perceive the traditional problem-solving methods help them. Solving the circuit problems is like making dinner - the tools and ingredients should be chosen carefully with a specific taste in mind. Recipes are predictable, repeatable and a wonderfully creative. When asking a junior about this analogy, the student responded: “Wow, that was a really good analogy. I never thought about it like that.” Analogies provide an interpretive bridge in comparing features of familiar and unfamiliar concepts.
In the first course on circuit analysis the foundational concepts include Ohm’s Law, Kirchhoff’s Laws and Nodal Analysis. To the end, if a student does not understand the fundamentals of elementary circuit analysis, they are unlikely to appreciate or learn more advanced topics.
In this first work-in-progress (WIP) report, the primary goal is to introduce an inventory of Circuit Teaching with Real World Analogies (CTRWA) for Electric Circuits I. Course modules with CTRWA will be developed to increase student perception of learning in the topics of voltage, current and power as well as Ohm’s Law, superposition and the Thevenin equivalent circuit. For example, Kirchhoff’s Voltage Law CTRWA will include a running track analogy while KCL will use a ‘recipe' to find the ingredients of a parallel circuit in order to “make the cake” (solution). The analogies will be applied with consideration to students’ prior knowledge and cognitive resources.
Using surveys and interviews as well as test question responses, this WIP is guided by several questions: 1) What are students’ perception of learning Ohm’s Law, KVL and KCL with real-world analogies? 2) Are in-person quiz scores with analogies comparable to web-based multiple choice homework scores? 3) Are students more confident and knowledgeable about KVL, KCL, superposition and Thevenin equivalent circuits at the end of the semester? 4) What is the correlation between student academic success (grades) and student misconceptions gained from CTRWA.
The secondary goal is to seek feedback from the ECE teaching community who teach juniors and seniors in Circuit Analysis II and Electronics who would be the potential users of the CTRWA instrument. This WIP provides an opportunity for the engineering community and curriculum designers to innovate instructional design and create methodologies to remedy potential misconceptions in Electric Circuits.

Authored by
Dr. Christopher Horne (North Carolina A&T State University (CoE))

The rapid integration of Artificial Intelligence (AI) into engineering practice necessitates
critically examining our educational approaches. This paper presents an investigation into the
performance of Large Language Models (LLMs) within the context of our Electrical Engineering
(EE) and Computer Engineering (CpE) undergraduate curricula at [BLIND] University. Our
study addresses a fundamental question: How do current AI tools perform on typical course
assessments, and what implications does this have for curriculum design?
We introduce a systematic methodology for benchmarking LLM performance on our course
assessments, including exams, assignments, and projects. Utilizing state-of-the-art LLMs, we
evaluate their capabilities across core courses in our EE and CpE programs. This includes
Circuits I (ECE 205), Digital Design (ECE 287), Energy Systems (ECE 291), and Signals and
Systems (ECE 306). Our benchmarking results reveal the strengths and limitations of these AI
tools in engineering education tasks, providing insights for curriculum adaptation. We discuss
how these results might inform the evolution of engineering education, highlighting areas where
AI could enhance learning and where human skills should be reinforced. This work contributes to
the ongoing dialogue on AI integration in engineering education. It offers a first step in providing
a replicable framework for continuously assessing AI capabilities in academic settings and how
this activity can aid educators. As we navigate the transforming landscape of engineering practice
and education, such benchmarking efforts are essential for ensuring our curricula remain relevant
and effective in preparing the next generation of engineers for an AI-augmented profession.

Authored by
Dr. Peter Jamieson (Miami University), Dr. George D. Ricco (Miami University), Brian A Swanson (Miami University), and Dr. Bryan Van Scoy (Miami University)

The *** is a nonprofit organization that facilitates collective efforts through equitable partnerships between its 21 MSI core members, 15 PWI affiliate members, 8 corporate members and other collaborating organizations. The *** 2TO4 Project builds on its Pathways to Success program to support students who begin their studies at a community college (CC) or other 2-year institution by providing financial support, mentoring and other personalized transition support, professional guidance, and community engagement.

The 2TO4 network of CCs consists of 20 sub-networks built around the 20 4-year MSIs that are core *** members. The vision of 2TO4 is to double the total number of students following this pathway to their BS degree in ECE by sharing promising practices and providing robust transition support infrastructure and increased financial support for CC students. Participating CCs become members of *** and engage in equitable partnerships with 4-year MSIs and PWIs, industry and DoD labs to implement the various building blocks of 2TO4.

During the first year of this multi-year effort, a base version of 2TO4 was created. Program leadership began working through institutional challenges with the 60+ program partners, regular meetings were scheduled, and a general communication infrastructure was rolled out. The first cohort of more than two dozen student participants was selected along with individual faculty and staff who create and deliver student support resources.

During the second year of the project, the number of participating students increased and support infrastructure and programs expanded. The guiding principles of Asset Driven Equitable Partnerships (ADEP) were used to build connections between MSIs and CCs by co-creating and co-delivering new learning opportunities. A new seminar series was developed on Talking about Transforming Transfer: Re-conceptualizing Engineering Transfer to Broaden Participation in Engineering along with a series of in-person and online facilitated meetings on how dedicated engineering transfer professionals can learn to do their job Better Together.
In the third year of the project, new programmatic elements are focused on addressing the very different student experiences at the 20 local hubs because transfer processes and local support infrastructures vary greatly by institution and state. Supported students will be brought together at an in-person meeting in March 2025 to build common ground by having teams co-develop and co-deliver outreach activities, aided by academic and industry mentors. Finally, the hub-model is being implemented by *** industry members, built around schools they work with that have the potential to attract and retain more diverse students in ECE programs. This latter effort will impact both the quality and sustainability of the 2TO4 project.

2TO4 assessment is focused on the extent to which each programmatic component is implemented with fidelity and the program has built the necessary capacity to support students. Formative feedback from each participant is collected and student progress is tracked. Key to this stage of the project is building trust and equitable partnerships, along with making necessary programmatic changes. The developing student/faculty networks described above have resulted from the identification of both problems and opportunities.

Authored by
Dr. Kenneth A Connor (Rensselaer Polytechnic Institute), Prof. Miguel Velez-Reyes (University of Texas at El Paso), Dr. Barry J. Sullivan (Electrical & Computer Engineering Department Heads Assn), Elizabeth Hibbler (Conference for Industry and Education Collaboration (CIEC)), Michelle Klein (Electrical and Computer Engineering Dept. Heads Assoc. (ECEDHA)), Dr. Bruk T Berhane (Florida International University), and Prof. Petru Andrei (Florida A&M University - Florida State University)

Traditional office hours in undergraduate engineering courses often presents several challenges. Typically structured as one-on-one interactions between the student and the instructor, they tend to be transactional, focusing on quick clarifications rather than deep engagement. This setup may not facilitate collaborative learning or peer-to-peer interaction, missing an opportunity for students to learn from each other’s questions and experiences. Additionally, low attendance is a common issue, which further limits the potential impact of these sessions on student learning and engagement.

We believe that students are motivated to attend office hours due to a combination of multiple recommended practices in STEM education being intentionally implemented in lectures starting from the first interaction. These practices include learning all student names (or as many as possible), being approachable, sometimes funny, being invested in student learning experience, talking to students outside class, chatting about their activities, being willing to help, respecting students and their privacy, pointing out errors gently and announcing strengths, and asking homework questions that promote mental health and self-care.

These practices provide a foundation that sparks students’ interest in lecture topics, inspires them to raise questions, and provokes subsequent discussion to fill gaps in understanding. Moreover, crafting homework questions that encourage students to seek support from the teaching team provides a strong incentive for them to attend office hours.

During office hours, the teaching team upholds the commitments made during lectures, aligning their approach with recommended best practices. The atmosphere of the active group office hours is vibrant and enthusiastic. To accommodate as many students as possible, we utilize a spacious setting with round tables, allowing for easy circulation and support. We promote collaboration and peer learning by organizing seating based on specific topics or homework problem numbers. Office hours typically last for two hours each week, and we also offer individual appointments for students seeking private assistance. This format of office hours can also serve as a space for fostering a sense of community and belonging among students. In departments without a structured cohort system, office hours can become a central hub for students to build connections and develop a supportive network.

Students who participated in this format of office hours have expressed that these sessions were beneficial. During Autumn 2023, in addition to knowledge transfer, office hours evolved into a social space where students could relax, connect with peers, seek career guidance, expand their professional networks, and stay engaged with the teaching team. This was particularly significant since many of these students belonged to engineering departments other than that of the teaching team. Many of these connections persist even a year after the course has been offered.

Our goal is to help educators in deciding whether active group office hours might benefit them and to guide the adaptation and adoption of the approach.

Our research questions are:

“What motivates undergraduate students to attend active group office hours?”

“How can active group office hours contribute to undergraduate students’ course success, engagement, and interest in the discipline?”

We plan to use E E 215: Fundamentals of Electrical Engineering at the University of Washington (UW), Seattle as the test case for this study. This a cross-departmental course for various engineering majors, taught by Electrical and Computer Engineering faculty. During the autumn term, class sizes can reach up to 300 students, with more than half typically consisting of mechanical engineering students. We intend to use surveys as our primary measurement tool, administering them biweekly. Additionally, if students express an interest in contributing to the study, we plan to conduct follow-up interviews with them. The research surveys, their administration, and the follow-up interviews will be conducted in collaboration with the Office for the Advancement of Engineering Teaching and Learning (ET&L) at the University of Washington.

When properly administered, these surveys can offer valuable insights specifically for office hours and can also inform best practices for conducting quiz sections and review sessions, which may be of interest to the broader engineering education community.

Authored by
Dr. Mahmood Hameed (University of Washington, Seattle), Dr. Ken Yasuhara (University of Washington), and Dr. Devshikha Bose (University of Washington-Seattle)

This paper presents the development and implementation of a remote Field-Programmable Gate
Array (FPGA) lab system, designed to provide students with flexible, remote access to FPGA
hardware. By integrating the Altera DE1 Board with an in-house designed and developed Digital
Design Trainer (DDT) board, the system allows students to engage with FPGA technology from
any location, overcoming the limitations of traditional on-site labs. The remote lab enables real-
time FPGA programming through a web-based interface and live camera feedback, replicating
the in-person lab experience.
In traditional labs, students are typically restricted to two to three hours of lab time, often leaving
insufficient time to explore beyond the core lab assignments. With the Remote FPGA Lab,
students can experiment with course concepts at their own pace, ensuring equitable access to
hands-on FPGA experience regardless of geographic location. This system enhances students'
technical skills and better prepares them for careers in fields requiring custom hardware
solutions.
The initial evaluation of the system has shown promising results. A pilot study with a small
group of students is conducted with valuable feedback, which is used to enhance the system’s
design. In the current semester, students in the digital systems course use the Remote FPGA Lab,
and their feedback is collected to refine further and optimize the system.

Authored by
Mr. Ze Yang (University Of Toronto) and Dr. Hamid S Timorabadi P.Eng. (University of Toronto)