The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
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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In the citing paper
“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]
In the citing paper
“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]
Citations
Cites
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101
Tsai, M., Ong, C. W., & Chen, C. (2023) · Education for Chemical Engineers · doi:10.1016/j.ece.2023.05.001 · Cited by 226
Kabir, S., Udo-Imeh, D. N., Kou, B., & Zhang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642596 · Cited by 160
Teaching Academic Honesty in CS50
Malan, D. J., Yu, B., & Lloyd, D. (2020) · Proceedings of the 51st ACM Technical Symposium on Computer Science Education · doi:10.1145/3328778.3366940 · Cited by 15
Self-citationEvaluating Feedback Tools in Introductory Programming Classes
Reis, R., Soares, G., Mongiovi, M., & Andrade, W. L. (2019) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie43999.2019.9028418 · Cited by 13
Differentiating for Comfort with Computer Science
Zenke, C., & Malan, D. J. (2022) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3545947.3573249 · Cited by 2
Self-citation
Research works printed but not linked: 3
- Wei Dai Jionghao Lin Flora Jin Tongguang Li Yi-Shan Tsai Dragan Gasevic and Guanliang Chen. 2023. Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT. https://doi.org/10.35542/osf.io/hcgzj · DOI · Paper page
- Samia Kabir David N. Udo-Imeh Bonan Kou and Tianyi Zhang. 2023. Who Answers It Better? An In-Depth Analysis of ChatGPT and Stack Overflow Answers to Software Engineering Questions. arxiv: 2308.02312 [cs.SE] · arXiv
- Stephanie Lin Jacob Hilton and Owain Evans. 2022. Teaching Models to Express Their Uncertainty in Words. arxiv: 2205.14334 [cs.CL] · arXiv
Cited by
Sorted by citations
Systematic review of artificial intelligence in education: trends, benefits, and challenges
Garzón, J., Patiño, E., & Marulanda, C. (2025) · Multimodal Technologies and Interaction · doi:10.3390/mti9080084 · Cited by 265
“The substantial representation of ICT aligns with prior literature that identifies a natural synergy between AI and computer science education [55].”
Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda
Bittle, K., & El-Gayar, O. (2025) · Information · doi:10.3390/info16040296 · Cited by 305
“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.”
The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers
Prather, J., Reeves, B. N., Leinonen, J., MacNeil, S., Randrianasolo, A. S., Becker, B. A., Kimmel, B., Wright, J., & Briggs, B. (2024) · Proceedings of the 2024 ACM Conference on International Computing Education Research - Volume 1 · doi:10.1145/3632620.3671116 · Cited by 196
An LLM-Driven Chatbot in Higher Education for Databases and Information Systems
Neumann, A. T., Yin, Y., Sowe, S. K., Decker, S., & Jarke, M. (2024) · IEEE Transactions on Education · doi:10.1109/te.2024.3467912 · Cited by 209
Retrieval-augmented generation for educational application: A systematic survey
Li, Z., Wang, Z., Wang, W., Hung, K., Xie, H., & Wang, F. L. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100417 · Cited by 151
Metadata
- 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
- Social Sciences › Education › 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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