Instructor Perceptions of AI Code Generation Tools - A Multi-Institutional Interview Study

Judy Sheard, Paul Denny, Arto Hellas, Juho Leinonen, Lauri Malmi, Simon

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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Much of the recent work investigating large language models and AI Code Generation tools in computing education has focused on assessing their capabilities for solving typical programming problems and for generating resources such as code explanations and exercises. If progress is to be made toward the inevitable lasting pedagogical change, there is a need for research that explores the instructor voice, seeking to understand how instructors with a range of experiences plan to adapt. In this paper, we report the results of an interview study involving 12 instructors from Australia, Finland and New Zealand, in which we investigate educators' current practices, concerns, and planned adaptations relating to these tools. Through this empirical study, our goal is to prompt dialogue between researchers and educators to inform new pedagogical strategies in response to the rapidly evolving landscape of AI code generation tools.

Original languageEnglish
Title of host publicationSIGCSE 2024 - Proceedings of the 55th ACM Technical Symposium on Computer Science Education
Number of pages7
ISBN (Electronic)979-8-4007-0423-9
Publication statusPublished - 7 Mar 2024
MoE publication typeA4 Conference publication
EventACM Technical Symposium on Computer Science Education - Portland, United States
Duration: 20 Mar 202423 Mar 2024
Conference number: 55


ConferenceACM Technical Symposium on Computer Science Education
Abbreviated titleSIGCSE
Country/TerritoryUnited States


  • ai code generation
  • generative ai
  • instructor perceptions
  • interview study
  • large language models
  • llms
  • programming education


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