ME 138: Concurrent Product & Process Design

Team-Based Learning, Active Learning, Using AI Tools

Course Description

Manufacturing considerations in product design including: design for manufacturing (DFM), design for assembly (DFA), design to cost (DTC), design to life cycle cost (DTLCC), design for quality and reliability (DFQR); introduction to concurrent engineering. Two hours lecture, three-hour lab. Upper division.

Overview of the Redesign Project

The Concurrent Product & Process Design course was redesigned to improve student engagement, collaboration, and learning effectiveness in a hands-on, design-centered environment. The redesign focused on three key elements: classroom learning activities, team-based project collaboration, and assessment and instructional methods using AI. To promote active learning, I integrated in-class exercises and activities where students immediately applied lecture concepts through short tasks that is also counted toward participation and attendance grades. To strengthen team-based learning, students collaborated on product design projects where each member contribute specialized skills such as CAD modeling and design for manufacturability. Finally, I integrated AI tools to enhance both instruction and assessment. These include creating podcast-style AI dialogs to explain complex topics - and AI-generated rubrics that improve grading consistency. These changes align with departmental and college goals to foster experiential learning, digital literacy, and collaboration, while addressing the broader campus initiative to integrate emerging technologies in engineering education.

Assessment of the Redesign Project

Although the course redesign is still in progress, several assessment approaches have been implemented to evaluate its impact on student learning. The addition of in-class activities has provided immediate feedback on students' understanding and engagement, reflected in improved performance on lab assignments and graded assignments. AI tools were used to form diverse teams, generate project topics, and create objective rubrics for evaluating team projects, ensuring fair and consistent assessment across varied designs. Additionally, AI-generated lecture materials, such as podcast-style explanations, have offered students alternative ways to grasp complex and less exciting concepts that are often difficult to grasp in traditional classroom settings. These approaches are expected to enhance comprehension and retention.

This table shows three ways I assessed my course redesign.
Active Learning	Team-Based Learning	Use of AI Tools
In-Class Activities immediately related to the lecture concepts. This results in improved performance on lab assignments and graded assignments.	A Rubric for team-based projects for objective grading. And a report scoring sheet.	Podcast-style lecture for student engagement, providing alternative ways to understand complex and less exciting concepts in traditional lecture settings.

Reflection

The introduction of active-learning activities has increased student participation and engagement during class sessions, as students are applying concepts immediately rather than passively listening (also by using it as an attendance checking tool). The use of AI-generated materials, such as podcast-style lectures, has made abstract or traditionally less engaging topics - like design for manufacturability guidelines - more accessible and appealing to students. Team-based learning has also enhanced collaboration and peer-to-peer teaching, with students learning to leverage each other's strengths in areas such as CAD modeling and product design. Overall, these changes have fostered a more interactive and dynamic learning environment that aligns with the goals of preparing students for real-world, collaborative engineering work.

Several challenges include: Managing time for both lectures and in-class team activities proved difficult, sometimes limiting the depth of hands-on practice, especially applying team-based peer-learning techniques. While AI-assisted grading rubrics improved consistency, assessing diverse team projects remains complex due to the wide variation in design topics. I plan to refine the balance between lecture and activity time, incorporate more structured peer-learning components, and further explore AI tools for individualized feedback and adaptive learning support.

Faculty Biography

This table includes my faculty biography.
	Dr. Yong Suh earned a Ph.D. in Mechanical Engineering from Rensselaer Polytechnic Institute in Troy, NY. Before joining academica, he worked as a senior software engineer at SDRC and SolidWorks, where he contributed to the development of Computer-Aided Design (CAD) software. He is currently a faculty member in the Department of Mechanical Engineering, specialize in mechanical design, design automation, CAD/CAM/CAE, and additive manufacturing. His teaching and research focus on integrating modern design tools and digital technologies to enhance engineering education and product development processes.

**Program Sponsors**
	This work was supported by: Sacramento State Academic Affairs - Center for Teaching and Learning