Assessment in CS50 with AI: Leveraging Generative Artificial Intelligence for Personalized Student Evaluation

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.

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Publication Details

Published
2025-02-18
DOI
https://doi.org/10.1145/3641555.3705061
Citations
1
Primary Topic
Intelligent Tutoring Systems and Adaptive Learning
Type
article
Field-Weighted Citation Impact
2.48
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article

Assessment in CS50 with AI: Leveraging Generative Artificial Intelligence for Personalized Student Evaluation

Christopher Perez, David J. Malan, Benjamin W. Xu, Rongxin Liu et al.
1 citations
Intelligent Tutoring Systems and Adaptive Learning
2.48
article

Assessment in CS50 with AI: Leveraging Generative Artificial Intelligence for Personalized Student Evaluation

Christopher Perez, David J. Malan, Benjamin W. Xu, Rongxin Liu, Yuliia Zhukovets, Jijun Zhao
article en
1 citations

Abstract

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.

Harvard University Press (US), Yale University (US)
Openalex Percentile: Top 12%
Intelligent Tutoring Systems and Adaptive Learning
2.48
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Assessment in CS50 with AI: Leveraging Generative Artificial Intelligence for Personalized Student Evaluation — Christopher Perez, David J. Malan, et al. · (2025) | TGRS Research Map | TGRS