Computer Science Education in the Age of Generative AI

2025-07-02

AI Summary

Generative AI (e.g., ChatGPT, Codex) presents dual challenges in computer science education—enhancing programming instruction, innovative assessment, and personalized feedback, while simultaneously threatening academic integrity, eroding foundational competencies, and complicating originality verification. Method: Through empirical analysis, LLM-driven experiments on code generation, debugging, and explanation, and educational data modeling, this study develops an integrated “teaching–assessment–governance” framework. Contribution/Results: It pioneers an AI-augmented pedagogical paradigm that concurrently fosters computational thinking and upholds academic integrity, supported by actionable curriculum integration strategies and a dynamic assessment guideline. Furthermore, it delivers China’s first systematic policy recommendations for AI in education, enabling human-AI collaborative, student-centered reform in new engineering education.

Technology Category

• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Game Design — Procedural Content Generation & Storytelling

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Examining AI's impact on computer science education
• Addressing challenges like academic integrity and over-reliance
• Proposing integration strategies and assessment best practices

Innovation

• Utilizing LLMs for code generation and debugging
• Integrating AI into pedagogical practices
• Developing policies for AI-enhanced education integrity