The Rest of the Robots: Generative AI in Post-introductory Computing Education

Generative AI (GenAI) is playing an increasingly influential role in computing education across all levels, offering new opportunities to support both teaching and learning. However, its effective integration raises critical concerns related to trust, academic integrity, and broader social and ethical implications. While substantial attention has been given to GenAI use in introductory programming courses (e.g., CS0/CS1), there remains a notable gap in research addressing its application in upper-level computing courses, such as software engineering, human-computer interaction, algorithms, operating systems, and theoretical computer science. This working group report presents two complementary studies: A systematic literature review of GenAI interventions in upper-level computing education, and a survey of computing instructors on their practices and perspectives regarding GenAI integration in these contexts. Based on the combined findings, this report presents an overview of current practice and practical guidance for computing instructors. The report is intended to inform the design of engaging, pedagogically sound, and forward-looking curricula that align with modern educational and workforce standards and expectations.

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Published
2025-06-27
DOI
https://doi.org/10.1145/3760545.3783970
Primary Topic
Teaching and Learning Programming
Type
article
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article

The Rest of the Robots: Generative AI in Post-introductory Computing Education

Dennis Bouvier, Raymond Pettit, Richard Glassey, Alexander Steinmaurer et al.
Teaching and Learning Programming
article

The Rest of the Robots: Generative AI in Post-introductory Computing Education

Dennis Bouvier, Raymond Pettit, Richard Glassey, Alexander Steinmaurer, Nuno Pombo, Charanya Ramakrishnan, Shubbhi Taneja, Ebrahim Rahimi, Olga Petrovska, Ryan E. Dougherty, Bruno Pereira Cipriano, Annapurna Vadaparty, Anastasiia Birillo, Emma Anderson, Muhammad Usman
article en

Abstract

Generative AI (GenAI) is playing an increasingly influential role in computing education across all levels, offering new opportunities to support both teaching and learning. However, its effective integration raises critical concerns related to trust, academic integrity, and broader social and ethical implications. While substantial attention has been given to GenAI use in introductory programming courses (e.g., CS0/CS1), there remains a notable gap in research addressing its application in upper-level computing courses, such as software engineering, human-computer interaction, algorithms, operating systems, and theoretical computer science. This working group report presents two complementary studies: A systematic literature review of GenAI interventions in upper-level computing education, and a survey of computing instructors on their practices and perspectives regarding GenAI integration in these contexts. Based on the combined findings, this report presents an overview of current practice and practical guidance for computing instructors. The report is intended to inform the design of engaging, pedagogically sound, and forward-looking curricula that align with modern educational and workforce standards and expectations.

Worcester Polytechnic Institute (US), University of Beira Interior (PT), United States Military Academy (US), Northumbria University (GB), University of California San Diego (US), Swansea University (GB), University of Arts and Industrial Design Linz (AT), United States Air Force Academy (US), University of Virginia (US), Blekinge Institute of Technology (SE), Open University of the Netherlands (NL), KTH Royal Institute of Technology (SE), Universidade Lusófona (PT), Macquarie University (AU)
Quality Education
Openalex Percentile: Top 20%
Teaching and Learning Programming
DOIOpen Access PDFOpenAlex
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