Puthumanaillam, Gokul (University of Illinois Urbana-Champaign), Bretl, Timothy (University of Illinois at Urbana-Champaign), Ornik, Melkior (Univ. of Illinois Urbana-Champaign)

The Lazy Student's Dream: ChatGPT Passing an Engineering Course on Its Own

Scheduled for presentation during the Regular Session "Challenges for Control Engineering Curricula" (FrAT1), Friday, June 20, 2025, 10:50−11:00, Room F09

14th IFAC Symposium on Advances in Control Education, June 17-21, 2025, Budapest, Hungary

This information is tentative and subject to change. Compiled on June 20, 2025

Abstract

This paper presents a comprehensive investigation into the capability of Large Language Models (LLMs) to successfully complete a semester-long undergraduate control systems course. Through evaluation of 115 course deliverables, we assess LLM performance using ChatGPT under a ``minimal effort" protocol that simulates realistic student usage patterns. The investigation employs a rigorous testing methodology across multiple assessment formats, from auto-graded multiple choice questions to complex Python programming tasks and long-form analytical writing. Our analysis provides quantitative insights into AI's strengths and limitations in handling mathematical formulations, coding challenges, and theoretical concepts in control systems engineering. The LLM achieved a B-grade performance (82.24%), approaching but not exceeding the class average (84.99%), with strongest results in structured assignments and greatest limitations in open-ended projects. The findings inform discussions about course design adaptation in response to AI advancement, moving beyond simple prohibition towards thoughtful integration of these tools in engineering education. Additional materials including syllabus, examination papers, design projects, and example responses can be found at the project website: https://gradegpt.github.io.