Large Language Models in Electrical and Computer Engineering Education: Opportunities, Challenges and the Road Ahead

ABSTRACT Electrical and Computer Engineering (EE/CE) education encompasses the rigorous study of electronic systems, computing architectures, embedded platforms, signal processing and hardware–software integration. Its inherently multidisciplinary nature demands mastery of mathematical modelling and experimental validation skills that pose substantial cognitive and logistical challenges to learners and instructors alike. The rapid emergence of Large Language Models (LLMs) offers transformative potential for mitigating these challenges, yet their integration into EE/CE pedagogy remains underexplored. This paper delivers a domain‐specific analysis of LLM adoption in EE/CE education. We first classify pedagogical applications across teaching, learning, and course‐specific use cases by highlighting their role in programming, simulation, circuit design, IoT/embedded systems and capstone projects. We then identify measurable benefits, including accelerated skill acquisition and improved student autonomy, while rigorously examining risks such as academic integrity concerns and overreliance. Through structured evaluation, we reveal current research gaps, most notably the scarcity of empirical studies oriented toward EE/CE and the absence of standardized performance metrics. Building on these findings, we propose best‐practice implementation models and articulate concrete future research directions, from EE/CE‐specific fine‐tuning to ethical governance frameworks. This paper is carefully designed to serve as a comprehensive reference and strategic roadmap for educators and students alike to support responsible and high‐impact LLM integration in EE/CE education.