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Explaining Code Examples in Introductory Programming Courses: LLM vs Humans

EasyChair Preprint no. 12117

11 pagesDate: February 14, 2024


Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide explanations for many examples typically used in a programming class. In this paper, we assess the feasibility of using LLMs to generate code explanations for passive and active example exploration systems. To achieve this goal, we compare the code explanations generated by chatGPT with the explanations generated by both experts and students.

Keyphrases: ChatGPT, Code Explanations, programming, worked examples

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Arun Balajiee Lekshmi Narayanan and Priti Oli and Jeevan Chapagain and Mohammad Hassany and Rabin Banjade and Peter Brusilovsky and Vasile Rus},
  title = {Explaining Code Examples in Introductory Programming Courses: LLM vs Humans},
  howpublished = {EasyChair Preprint no. 12117},

  year = {EasyChair, 2024}}
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