Teaching CS50 with AI: Leveraging Generative Artificial Intelligence in Computer Science Education
Abstract
In Summer 2023, we developed and integrated a suite of AI-based software tools into CS50 at Harvard University. These tools were initially available to approximately 70 summer students, then to thousands of students online, and finally to several hundred on campus during Fall 2023. Per the course's own policy, we encouraged students to use these course-specific tools and limited the use of commercial AI software such as ChatGPT, GitHub Copilot, and the new Bing. Our goal was to approximate a 1:1 teacher-to-student ratio through software, thereby equipping students with a pedagogically-minded subject-matter expert by their side at all times, designed to guide students toward solutions rather than offer them outright. The tools were received positively by students, who noted that they felt like they had "a personal tutor.'' Our findings suggest that integrating AI thoughtfully into educational settings enhances the learning experience by providing continuous, customized support and enabling human educators to address more complex pedagogical issues. In this paper, we detail how AI tools have augmented teaching and learning in CS50, specifically in explaining code snippets, improving code style, and accurately responding to curricular and administrative queries on the course's discussion forum. Additionally, we present our methodological approach, implementation details, and guidance for those considering using these tools or AI generally in education.
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GenAI could serve as a tool to reduce academic dishonesty by offering guidance that encourages meaningful engagement with the material rather than providing direct answers.
“Additionally, Liu et al. [18] suggest that GenAI could serve as a tool to reduce academic dishonesty by offering guidance that encourages meaningful engagement with the material rather than providing direct answers.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [18]
The substantial representation of ICT aligns with prior literature that identifies a natural synergy between AI and computer science education.
“The substantial representation of ICT aligns with prior literature that identifies a natural synergy between AI and computer science education [55].”
From Systematic review of artificial intelligence in education: trends, benefits, and challenges (2025) · cites this paper as [55]
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Weichert, J., & Eldardiry, H. (2026) · AI and Ethics · doi:10.1007/s43681-026-01021-6 · Cited by 2 · see citing works
Fine-Tuning Large Language Models for Better Programming Error Explanations
Vassar, A., Renzella, J., Ross, E., & Taylor, A. (2024) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3699538.3699581 · Cited by 2 · see citing works
Teaching Generative AI for Cybersecurity: A Project-Based Learning Approach
Mathews, N., Schwartz, C., & Wright, M. (2025) · Journal of The Colloquium for Information Systems Security Education · doi:10.53735/cisse.v12i1.211 · Cited by 2 · see citing works
Concepts for Teaching Software Development in the Age of AI-Tools
Böttcher, A., Thurner, V., & Zönnchen, B. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016505 · Cited by 2 · see citing works
Fowler, M., Emeka, C., Chen, B., Smith, D. H., West, M., & Zilles, C. B. (2025) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3774752 · Cited by 2 · see citing works
Student Engagement with GenAI's Tutoring Feedback: A Mixed Methods Study
Jacobs, S., Haas, J., & Kiesler, N. (2025) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3769994.3770034 · Cited by 6 · see citing works
Let’s Chat about This - A Template for Well-Aligned AI Integration in Schools
Martin, S., Lauber, A.-M., & Pampel, B. (2025) · IEEE German Education Conference (GECon) · doi:10.1109/gecon64629.2025.11369312 · Cited by 2 · see citing works
Teaching OS315 with Chatbots: Instructional Design, Deployment, Cost & Student AttitudesPreprint
Berengueres, J. O. L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4765343 · Cited by 2 · see citing works
Birillo, A. (2024) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3699538.3699571 · Cited by 1 · see citing works
Use of ChatBots in Programming Education: A Scoping Review
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Innovations Focusing on Artificial Intelligence in Computer Science Education
Erdağı, E. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-4542-9.ch005 · Cited by 1 · see citing works
Torek, A. J., Sorensen, E., Hahle, N., & Kennington, C. (2024) · Frontiers in Education Conference · doi:10.1109/fie61694.2024.10893165
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Zönnchen, B., Hobelsberger, M., Socher, G., Thurner, V., & Ottinger, S. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016571 · Cited by 1 · see citing works
- Authors
- Liu, R., Zenke, C., Liu, C., Holmes, A., Thornton, P., & Malan, D. J.
- Affiliations
- Harvard University (US)
- Yale University (US)
- Venue
- Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
- Publisher
- ACM
- Published
- 2024-03-07
- Pages
- 750-756
- OpenAlex
- W4392563703
- Cited by
- 185
- References
- 5
- Citations by year
- 2026: 58 · 2025: 90 · 2024: 37
- FWCI
- 70.29
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- generative AI in education, computer science education, code explanation, student perceptions
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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