Computer Science Education in the Age of Generative AI
Russell Beale

TL;DR
This paper explores how generative AI tools like LLMs are transforming computer science education by offering new opportunities and challenges, and provides recommendations for integrating AI responsibly into curricula.
Contribution
It offers a comprehensive analysis of AI's impact on CS education, including pedagogical strategies, assessment methods, and policy recommendations, supported by empirical data.
Findings
AI enhances coding assistance and pedagogical practices
Challenges include academic integrity and verifying originality
Recommendations for responsible AI integration in curricula
Abstract
Generative AI tools - most notably large language models (LLMs) like ChatGPT and Codex - are rapidly revolutionizing computer science education. These tools can generate, debug, and explain code, thereby transforming the landscape of programming instruction. This paper examines the profound opportunities that AI offers for enhancing computer science education in general, from coding assistance to fostering innovative pedagogical practices and streamlining assessments. At the same time, it highlights challenges including academic integrity concerns, the risk of over-reliance on AI, and difficulties in verifying originality. We discuss what computer science educators should teach in the AI era, how to best integrate these technologies into curricula, and the best practices for assessing student learning in an environment where AI can generate code, prototypes and user feedback. Finally,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
