AI-Enhanced Project-Based Learning in Embedded Systems Education

The rapid evolution of embedded and intelligent systems has led to an increasing demand for engineers proficient in both hardware and software design. Traditional lecture-based education often falls short in providing students with hands-on experience, interdisciplinary collaboration, and AI-driven learning support. This project proposes a project-based learning approach within the Embedded Systems course at ETH Zurich, which has a large and diverse cohort of 380 students, forecast to increase, spanning disciplines such as computer science, mechanical engineering, and electrical engineering. The goal is to enhance learning through hands-on embedded systems development while leveraging AI-powered tools to support students throughout their learning journey.

This project plans to
– Develop an Open Educational Embedded Platform: Design and implement an expansion board with sensors, which will serve as a common foundation for multiple courses (e.g., robotics, wearables, biomedical applications, control systems).
– Integrate AI for Personalized Learning: Fine-tune an LLM-based AI assistant using past exams, course materials, and documentation to provide students with intelligent guidance during both the project phase
and the learning phase.
– Promote Competency-Based Education: Shift from traditional exams to a project-based assessment, fostering problem-solving, creativity, and interdisciplinary teamwork.

This project aims to develop a PBL AI-driven learning approach for a large and heterogeneous cohort, integrating hands-on embedded systems education with AI-powered support.
Main goals:
– Developing an Open Educational Embedded Platform
– Students, including TAs and thesis researchers, will contribute to designing and testing an expansion board for multi-course use Fine-Tuning an AI Learning Assistant
– PhD and Master’s students specializing in AI and LLMs will help refine an AI model trained on course materials for personalized support Optimizing PBL Implementation
– Students with prior PBL experience will provide feedback and assist in structuring hands-on projects for large, interdisciplinary cohorts Evaluating and Scaling the Approach
– Implementing this in 2 Embedded Systems(2025&2026). Student feedback and engagement metrics will guide improvements, ensuring scalability and effectiveness across ETH programs

This project enhances student learning by integrating PBL with AI-driven support, fostering hands-on embedded systems experience across a heterogeneous cohort.

Lecturers benefit from scalable, reusable tools that improve student engagement and streamline teaching.

The modular embedded platform supports multiple disciplines, making it valuable across degree programs. By advancing AI in education, this initiative creates a replicable model for integrating PBL and competency-based learning at ETH.