Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book

Stephen Macneil, Andrew Tran, Arto Hellas, Joanne Kim, Sami Sarsa, Paul Denny, Seth Bernstein, Juho Leinonen

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

140 Citations (Scopus)
34 Downloads (Pure)

Abstract

Advances in natural language processing have resulted in large language models (LLMs) that can generate code and code explanations. In this paper, we report on our experiences generating multiple code explanation types using LLMs and integrating them into an interactive e-book on web software development. Three different types of explanations - a line-by-line explanation, a list of important concepts, and a high-level summary of the code - were created. Students could view explanations by clicking a button next to code snippets, which showed the explanation and asked about its utility. Our results show that all explanation types were viewed by students and that the majority of students perceived the code explanations as helpful to them. However, student engagement varied by code snippet complexity, explanation type, and code snippet length. Drawing on our experiences, we discuss future directions for integrating explanations generated by LLMs into CS classrooms.

Original languageEnglish
Title of host publicationSIGCSE 2023 - Proceedings of the 54th ACM Technical Symposium on Computer Science Education
PublisherACM
Pages931-937
Number of pages7
ISBN (Electronic)978-1-4503-9431-4
DOIs
Publication statusPublished - 2 Mar 2023
MoE publication typeA4 Conference publication
EventACM Technical Symposium on Computer Science Education - Toronto, Canada
Duration: 15 Mar 202318 Mar 2023
Conference number: 54

Conference

ConferenceACM Technical Symposium on Computer Science Education
Abbreviated titleSIGCSE
Country/TerritoryCanada
CityToronto
Period15/03/202318/03/2023

Keywords

  • computer science education
  • explanations
  • large language models

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