Computer Science Education in the Age of Generative AI

• URL: http://arxiv.org/abs/2507.02183v1
• Date: Wed, 02 Jul 2025 22:28:45 GMT
• Title: Computer Science Education in the Age of Generative AI
• Authors: Russell Beale,
• Abstract summary: Generative AI tools like ChatGPT and Codex are rapidly revolutionizing computer science education.<n>This paper examines the profound opportunities that AI offers for enhancing computer science education.<n>It highlights challenges including academic integrity concerns, the risk of over-reliance on AI, and difficulties in verifying originality.
• Score: 1.2691047660244332
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• A Community-driven vision for a new Knowledge Resource for AI [59.29703403953085]
  Despite the success of knowledge resources like WordNet, verifiable, general-purpose widely available sources of knowledge remain a critical deficiency in AI infrastructure.<n>This paper synthesizes our findings and outlines a community-driven vision for a new knowledge infrastructure.
  arXiv  Detail & Related papers  (2025-06-19T20:51:28Z)
• Generative AI Literacy: Twelve Defining Competencies [48.90506360377104]
  This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI.<n>The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations.<n>These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly.
  arXiv  Detail & Related papers  (2024-11-29T14:55:15Z)
• Human-Centric eXplainable AI in Education [0.0]
  This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape. It emphasizes its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools. It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement.
  arXiv  Detail & Related papers  (2024-10-18T14:02:47Z)
• Computing in the Life Sciences: From Early Algorithms to Modern AI [45.74830585715129]
  This paper highlights key milestones and technological advancements through the historical development of computing in the life sciences. The discussion includes the inception of computational models for biological processes, the advent of bioinformatics tools, and the integration of AI/ML in modern life sciences research. Attention is given to AI-enabled tools used in the life sciences, such as scientific large language models and bio-AI tools, examining their capabilities, limitations, and impact to biological risk.
  arXiv  Detail & Related papers  (2024-06-17T21:36:52Z)
• Generative AI has lowered the barriers to computational social sciences [3.313485776871956]
  Generative artificial intelligence (AI) has revolutionized the field of computational social science (CSS)<n>Generative AI can significantly enhance the productivity of social scientists by automating the generation, annotation, and debug of code.<n>The educational sphere of CSS stands to benefit immensely from these tools, given their exceptional ability to annotate and elucidate complex codes for learners.
  arXiv  Detail & Related papers  (2023-11-17T19:24:39Z)
• Students' Perspective on AI Code Completion: Benefits and Challenges [2.936007114555107]
  We investigated the benefits, challenges, and expectations of AI code completion from students' perspectives. Our findings show that AI code completion enhanced students' productivity and efficiency by providing correct syntax suggestions. In the future, AI code completion should be explainable and provide best coding practices to enhance the education process.
  arXiv  Detail & Related papers  (2023-10-31T22:41:16Z)
• A Survey on Brain-Inspired Deep Learning via Predictive Coding [85.93245078403875]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks.<n>PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Are machine learning technologies ready to be used for humanitarian work and development? [2.156882891331917]
  Digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development. We argue that new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.
  arXiv  Detail & Related papers  (2023-07-04T19:32:35Z)
• What Students Can Learn About Artificial Intelligence -- Recommendations for K-12 Computing Education [0.0]
  Technological advances in the context of digital transformation are the basis for rapid developments in the field of artificial intelligence (AI) An increasing number of computer science curricula are being extended to include the topic of AI. This paper presents a curriculum of learning objectives that addresses digital literacy and the societal perspective in particular.
  arXiv  Detail & Related papers  (2023-05-10T20:39:43Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.