Assessment in CS50 with AI: Leveraging Generative Artificial Intelligence for Personalized Student Evaluation
Rongxin Liu, Benjamin W. Xu, Christopher Perez, Julianna Zhao, Yuliia Zhukovets, David J. Malan · 2025
The scalability challenges of code review and pair-programming assessments in large computer science courses, such as CS50 at Harvard University, have opened up opportunities for the application of Generative AI. Leveraging large language models (LLMs), CS50.ai offers a suite of AI-based tools that assist both students and instructors in mastering course material while overcoming the limitations posed by human resource constraints. This demo highlights how generative AI can be employed to conduct code reviews and pair-programming simulations, providing real-time feedback, code explanations, and collaborative programming insights. By integrating these AI tools into students' learning journeys, we aim to mimic the 1:1 interaction between instructor and student, improving both formative and summative assessments. We will showcase how these tools are implemented to scale personalized feedback, ensure academic integrity, and maintain pedagogical efficacy. Our presentation will also reflect on lessons learned from deploying these AI-driven tools in recent course offerings.