2026 ASEE Annual Conference & Exposition

The rapid integration of artificial intelligence (AI) and smart manufacturing technologies in modern industry demands a reexamination of how engineering students are trained for the evolving landscape of electronic manufacturing. In this domain, engineers must synthesize interdisciplinary knowledge spanning thermal management, mechanical integrity, and design-for-manufacturability (DFM). To meet these emerging educational needs, this study introduces an AI-powered collaborative simulation learning framework that bridges analytical design theory and intelligent manufacturing practice through a flipped, AI-augmented pedagogy. The framework was implemented and refined over three consecutive semesters in an undergraduate Electronics Packaging course, with student enrollments of 23, 21, and 22 respectively.
The instructional design follows an enhanced AI-flipped classroom model, integrating three progressive phases: (1) AI-assisted self-learning before class, (2) AI-supported group collaboration during class, and (3) simulation-based reflection and reporting after class. In the pre-class phase, students engage in self-directed study using conversational AI tools such as ChatGPT Team and Notion AI to review theoretical concepts, generate design questions, and draft preliminary design ideas. These AI tools provide formative feedback, summarize technical readings, and support self-regulated learning by prompting students to critically evaluate AI-generated explanations. This stage ensures that students enter the classroom prepared for higher-level application and problem solving.
During the in-class phase, students are organized into small design teams and participate in structured, AI-supported collaborative discussions. Guided by real-world problems such as thermal dissipation, vibration reliability, and manufacturability of electronic assemblies, each team debates alternative solutions with AI acting as a facilitator. The AI tools help synthesize diverse viewpoints, summarize ongoing discussions, and identify trade-offs between competing design criteria. For example, when evaluating materials for heat sinks or solder joint geometries for fatigue resistance, the AI generates comparative summaries that students analyze, refine, and validate through engineering reasoning. This collaborative environment fosters critical thinking, interdisciplinary communication, and manufacturability-centered problem solving.
In the post-class phase, students individually conduct detailed thermal, structural, and vibration simulations in SolidWorks and perform manufacturability evaluations using Autodesk AI Manufacturing. Tools such as Paperpal and Notion AI support report writing and visualization, allowing students to produce well-structured technical documentation. Each student integrates simulation data, manufacturability analysis, and reflective commentary into a comprehensive report that demonstrates both technical depth and metacognitive awareness. This iterative process—transitioning from AI-assisted preparation to team collaboration and individual design ex-tension—encourages deeper learning, accountability, and continuous reflection.
Evaluation across three semesters shows consistent improvement in simulation accuracy, manufacturability reasoning, and report quality. Students exhibited fewer modeling errors, stronger justifications for design choices, and more coherent technical communication. Qualitative feedback highlighted that AI tools enhanced conceptual clarity, organizational skills, and the ability to connect theoretical knowledge to manufacturing constraints. Importantly, students reported perceiving AI not as a substitute for human reasoning but as a collaborative partner that amplified learning and creativity.
This ongoing work contributes to the growing body of research on AI in engineering education by demonstrating how intelligent systems can augment—rather than automate—the learning process. The proposed framework aligns with ABET student outcomes, particularly in the application of engineering knowledge, teamwork, problem-solving, and technical communication. Future development will include creating quantitative instruments to assess AI literacy and manufacturability reasoning, as well as exploring predictive AI models for automated manufacturability scoring. By integrating AI-assisted self-learning, collaborative design, and simulation-based reflection, this framework represents a pedagogical innovation that equips students for intelligent, collaborative manufacturing systems in the era of Industry 5.0.