Computer Science Education in the Age of Generative AI

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, we propose a set of policy recommendations designed to harness the potential of generative AI while preserving the integrity and rigour of computer science education. Empirical data and emerging studies are used throughout to support our arguments.