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Kattis vs ChatGPT: Assessment and Evaluation of Programming Tasks in the Age of Artificial Intelligence
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0009-0005-3260-6036
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0009-0005-9000-2951
Utrecht University Hekla, Netherlands.
KTH, School of Electrical Engineering and Computer Science (EECS), Human Centered Technology, Media Technology and Interaction Design, MID.ORCID iD: 0000-0002-8543-3774
2024 (English)In: LAK 2024 Conference Proceedings - 14th International Conference on Learning Analytics and Knowledge, Association for Computing Machinery (ACM) , 2024, p. 821-827Conference paper, Published paper (Refereed)
Abstract [en]

AI-powered education technologies can support students and teachers in computer science education. However, with the recent developments in generative AI, and especially the increasingly emerging popularity of ChatGPT, the effectiveness of using large language models for solving programming tasks has been underexplored. The present study examines ChatGPT's ability to generate code solutions at different difficulty levels for introductory programming courses. We conducted an experiment where ChatGPT was tested on 127 randomly selected programming problems provided by Kattis, an automatic software grading tool for computer science programs, often used in higher education. The results showed that ChatGPT independently could solve 19 out of 127 programming tasks generated and assessed by Kattis. Further, ChatGPT was found to be able to generate accurate code solutions for simple problems but encountered difficulties with more complex programming tasks. The results contribute to the ongoing debate on the utility of AI-powered tools in programming education.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM) , 2024. p. 821-827
Keywords [en]
Academic Integrity, Automated Grading, ChatGPT, Programming Education
National Category
Educational Sciences
Identifiers
URN: urn:nbn:se:kth:diva-344554DOI: 10.1145/3636555.3636882Scopus ID: 2-s2.0-85187550433OAI: oai:DiVA.org:kth-344554DiVA, id: diva2:1845942
Conference
14th International Conference on Learning Analytics and Knowledge, LAK 2024, Kyoto, Japan, Mar 18 2024 - Mar 22 2024
Note

QC 20240321

 Part of ISBN 9798400716188

Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-03-21Bibliographically approved

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Dunder, NoraViberg, Olga

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