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Authoring Programming Exercises for Automated Assessment Assisted by Generative AI

Authors Yannik Bauer , José Paulo Leal , Ricardo Queirós

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Author Details

Yannik Bauer
  • DCC - FCUP, Porto, Portugal
José Paulo Leal
  • CRACS - INESC TEC, Porto, Portugal
  • DCC - FCUP, Porto, Portugal
Ricardo Queirós
  • CRACS - INESC TEC, Porto, Portugal
  • uniMAD - ESMAD, Polytechnic of Porto, Portugal

Funding

This work is co-funded by the Erasmus+ Programme of the European Union within the project FGPEPlusPlus, with Agreement Number 2023-1-PL01-KA220-HED-000164696.

Cite As Get BibTex

Yannik Bauer, José Paulo Leal, and Ricardo Queirós. Authoring Programming Exercises for Automated Assessment Assisted by Generative AI. In 5th International Computer Programming Education Conference (ICPEC 2024). Open Access Series in Informatics (OASIcs), Volume 122, pp. 21:1-21:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.ICPEC.2024.21

Abstract

Generative AI presents both challenges and opportunities for educators. This paper explores its potential for automating the creation of programming exercises designed for automated assessment. Traditionally, creating these exercises is a time-intensive and error-prone task that involves developing exercise statements, solutions, and test cases. This ongoing research analyzes the capabilities of the OpenAI GPT API to automatically create these components. An experiment using the OpenAI GPT API to automatically create 120 programming exercises produced interesting results, such as the difficulties encountered in generating valid JSON formats and creating matching test cases for solution code. Learning from this experiment, an enhanced feature was developed to assist teachers in creating programming exercises and was integrated into Agni, a virtual learning environment (VLE). Despite the challenges in generating entirely correct programming exercises, this approach shows potential for reducing the time required to create exercises, thus significantly aiding teachers. The evaluation of this approach, comparing the efficiency and usefulness of using the OpenAI GPT API or authoring the exercises oneself, is in progress.

Subject Classification

ACM Subject Classification
  • Applied computing → Computer-assisted instruction
  • Computing methodologies → Artificial intelligence
  • Applied computing → Interactive learning environments
Keywords
  • ChatGPT
  • generative AI
  • programming exercises
  • automated assessment

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