When AI Meets the Clock: Rethinking Learning and Assessment in Large-Scale Computing Courses

Abstract

As artificial intelligence (AI) tools like ChatGPT become more common, their role in computer science (CS) education continues to evolve, especially in large courses with time-limited assessments. This lightning talk presents an observation from a large-scale (over 300 students) introductory software design and engineering course at a university in the Southeastern United States. During a 30-minute, open-notes assessment where students were allowed to use generative AI, they were asked to extend one user story in an existing codebase they had previously built for the course. The task followed an all-or-nothing grading approach that required a fully functional user story implementation for credit. Although students often support using AI for learning, their reactions revealed a gap between what they expected AI to do and the actual thinking required to solve real problems quickly: Some students even expressed frustration, noting that AI tools offered little help under time pressure. To close the experience, we held a reflection lecture where students analyzed the role of AI as a tool and discussed its purpose in supporting augmented intelligence rather than replacing human reasoning. This case illustrates how assessment design can expose the limits of generative AI as a learning aid and highlights the importance of helping students build awareness of time, effort, and reflection when using these tools. The goal of this talk is to share these insights, invite discussion on AI use under time constraints, and explore how students adapt or resist adapting when AI cannot ''think fast enough'' for them.

Ähnliche Arbeiten