Optimizing Large Language Models for Auto-Generation of Programming Quizzes

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

Abstract

This study analyzes the use of Large Language Models (LLMs) like ChatGPT in creating quizzes for Java programming courses, specifically Object-Oriented Programming (CS1) and Data Structures (CS2). It aims to evaluate the accuracy of LLM-generated assessments, understand the benefits and drawbacks of using LLMs in CS education from educators' viewpoints, and identify effective prompt engineering strategies to enhance the quality of educational materials. The research compares quizzes made by LLMs against human-created content to assess their consistency with Java programming principles, alignment with CS1 and CS2 learning goals, and their impact on student engagement and comprehension, providing insights into LLMs' effectiveness in academic assessment creation for computer science education.

Original languageEnglish
Title of host publication2024 IEEE Integrated STEM Education Conference, ISEC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350352801
DOIs
StatePublished - 2024
Event14th IEEE Integrated STEM Education Conference, ISEC 2024 - Princeton, United States
Duration: 9 Mar 2024 → …

Publication series

Name2024 IEEE Integrated STEM Education Conference, ISEC 2024

Conference

Conference14th IEEE Integrated STEM Education Conference, ISEC 2024
Country/TerritoryUnited States
CityPrinceton
Period9/03/24 → …

Keywords

  • AI-Supplemental Instructor (AI-SI)
  • Java programming instruction
  • use of LLMs in CS education

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