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Cited by 14Teaching and Learning ProgrammingSoftware Engineering ResearchAcademic integrity and plagiarism

AI-Generated Code Not Considered Harmful

Open access
Kendon, Tyson, Wu, Leanne, Aycock, John·2023·Proceedings of the 25th Western Canadian Conference on Computing Education
DOI 10.1145/3593342.3593349
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Abstract

Recent developments in AI-generated code are merely the latest in a series of challenges to traditional computer science education. AI code generators, along with the plethora of available code on the Internet and sites that facilitate contract cheating, are a striking contrast to the heroic notion of programmers toiling away to create artisanal code from whole cloth.

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Integrity signals

Registry

Crossref · 10.1145/3593342.3593349

Cites

24 of 24 references verified0 citation errors · 0 not checkable

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Claims other papers make about this one

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  • Integrating GenAI tools into the learning process transforms them into a supplemental extension of the curriculum that enhances the development of higher-order skills with a focus on academic integrity.

    “Furthermore, Kendon et al. [20] advocate for integrating GenAI tools into the learning process, transforming them into a supplemental extension of the curriculum that enhances the development of higher-order skills with a focus on academic integrity.”

    From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [20]

    The Introduction (p. unavailable) considers actively bringing AI tools into computing pedagogy, while §5 (p. unavailable) says they open opportunities to use higher-order skills. Section 4 (p. unavailable) calls for renewed attention to academic integrity. The paper does not establish that integration enhances skill development or describe the tools as a supplemental extension of the curriculum; it says the ethics and effectiveness of proposed uses need study. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Explicit instruction on how to critically assess, refine, and debug AI-generated code fosters the development of essential competencies ranging from algorithmic reasoning to software testing.

    “Explicit instruction on how to critically assess, refine, and debug AI-generated code fosters the development of essential competencies -ranging from algorithmic reasoning to software testing (Denny et al., 2024;Kendon et al., 2023).”

    From Integrating Generative AI into Programming Education: Student Perceptions and the Challenge of Correcting AI Errors (2025) · cites this paper as Kendon et al., 2023

    Section 3.2 proposes having students assess AI-generated code, while Sections 3.1.3, 3.1.7, and 3.1.8 describe finding errors, creating tests, and fixing bugs in code. These support the proposed activities, but the paper does not establish that explicit instruction in refining and debugging AI-generated code develops the claimed range of competencies, particularly algorithmic reasoning. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Others have shifted the focus of assessment from product to process and have included interviews or presentations to determine students' depth of understanding.

    “Others have shifted the focus of assessment from product to process and have included interviews or presentations to determine students' depth of understanding [180, 304, 332].”

    From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [180]

    Section 3.2 proposes rewarding students’ analysis of code and using ungrading to reward process rather than product. Section 3.1.2 also proposes having students explain code to demonstrate understanding. The cited paper does not describe using interviews or presentations to assess students’ depth of understanding. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • Requiring that students declare when the production of their work has been assisted by GenAI and cite generated outputs appropriately helps preserve fairness while fostering ethical and responsible use.

    “Requiring that students declare when the production of their work has been assisted by GenAI and cite generated outputs appropriately helps preserve fairness while fostering ethical and responsible use [32, 180, 264].”

    From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [180]

    Section 4, “Upending Academic Integrity” (page unavailable), advocates clear guidance on citing external code, crediting contributors, and developing ethical conduct while allowing students to work with AI-generated code. It does not specifically call for students to declare GenAI assistance or cite generated outputs, or establish that those requirements preserve fairness. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Kendon et al. [180] argues that relying solely on detection tools inserts educators into an arms race with students, in which neither side can trust the other, and the winner is simply the one who brings the greatest and most advanced tools to bear.

    “Relying solely on such [detection] tools inserts educators into an arms race with students, in which neither side can trust the other, and the winner is simply the one who brings the greatest and most advanced tools to bear.”

    From Navigating the Ethical and Societal Impacts of Generative AI in Higher Computing Education (2025) · cites this paper as [180]

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Cites

Flagged first, then by citations

Research works printed but not linked: 13

  1. K. K. Agarwal and A. Agarwal. 2004. Do we need a separate assembly language programming course? Journal of Computing Sciences in Colleges 19, 4 (2004), 246-251.
  2. A. V. Aho, R. Sethi, and J. D. Ullman. 1986. Compilers: Principles, Techniques, and Tools. Addison-Wesley.
  3. J. Aycock, E. Pitout, and S. Storteboom. 2015. A Game Engine in Pure Python for CS1: Design, Experience, and Limits. In 20th ACM Annual Conference on Innovation and Technology in Computer Science Education. 93-98.
  4. J. Aycock, H. Wright, J. Hildebrandt, D. Kenny, N. Lefebvre, M. Lin, M. Mamaclay, S. Sayson, A. Stewart, and A. Yuen. 2019. Adapting the "Unessay" for Use in Computer Science. In Western Canadian Conference on Computing Education. Article 2, 6 pages.
  5. R. Clarke and T. Lancaster. 2013. Commercial Aspects of Contract Cheating. In 18th ACM Conference on Innovation and Technology in Computer Science Education. 219-224.
  6. D. E. Knuth. 1984. Literate Programming. Comput. J. 27, 2 (1984), 97-111.
  7. E. Lazarus. 2014. Developing reading and writing skills in a foreign language. In A Practical Guide To Teaching Foreign Language in the Secondary School, N. Pachler and A. Redondo (Eds.). Chapter 7.
  8. J. Lions. 1996. Lions' Commentary on UNIX 6th Edition with Source Code. Peer-to- Peer Communications.
  9. R. M. McClure. 1972. An appraisal of compiler technology. In AFIPS 1972 Spring Joint Computer Conference. 1-9.
  10. D. Parsons and P. Haden. 2006. Parson's Programming Puzzles: A Fun and Effective Learning Tool for First Programming Courses. In 8th Australasian Conference on Computing Education -Volume 52 (Hobart, Australia) (ACE '06). Australian Computer Society, Inc., AUS, 157-163.
  11. C. Riesbeck. 2020. Critique-Driven Learning and Assessment. See [9], Chapter 8, 123-139.
  12. P. H. Salus. 1994. A Quarter Century of UNIX. Addison-Wesley.
  13. K. Sierra, B. Bates, and T. Gee. 2022. Head First Java (3rd ed.). O'Reilly Media, Inc.

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Related

Authors
Kendon, T., Wu, L., & Aycock, J.
Affiliations
University of Calgary (CA)
ORCID
Venue
Proceedings of the 25th Western Canadian Conference on Computing Education
Publisher
ACM
Published
2023-05-04
Pages
1-7
DOI
10.1145/3593342.3593349
OpenAlex
W4384834951
Cited by
13
References
23
Citations by year
2026: 3 · 2025: 6 · 2024: 2 · 2023: 2
FWCI
3.93
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
Western Canadian Conference on Computing Education (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
  • Physical Sciences › Computer Science › Information Systems › Software Engineering Research
  • Social Sciences › Safety Research › Academic integrity and plagiarism
Keywords
AI-generated code, code generation, computer science education, academic integrity, contract cheating, programming education

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