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Unveiling the Impact of Large Language Models on Student Learning: A Comprehensive Case Study
University Ss. Cyril and Methodius, North Macedonia.ORCID iD: 0000-0002-9674-3081
Linnaeus University, Faculty of Technology, Department of Informatics. Linnaeus University, Linnaeus Knowledge Environments, Digital Transformations.ORCID iD: 0000-0001-7520-695X
Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).ORCID iD: 0000-0003-0372-7195
University Ss. Cyril and Methodius, North Macedonia.ORCID iD: 0000-0002-5422-7353
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2024 (English)In: 2024 IEEE Global Engineering Education Conference (EDUCON), IEEE, 2024Conference paper, Published paper (Refereed)
Sustainable development
SDG 4: Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
Abstract [en]

Large language models (LLMs) have achieved planetary popularity and have become accepted in higher education. On the basis of a survey that revealed the attitudes of students, reinforced by face-to-face interviews, and our own extensive academic background, we defined a realistic solution that enables the integration of LLMs for the creation of assignments. It embraces essay writing as well as various aspects of computer programming. The experiments were carried out during the winter semester of academic 2023/24 at two universities from two different countries. This paper unveils the experience gained in the creation of computer science assignments with and without the use of LLM. Comparative analysis refers on three approaches: traditional or manual assignment preparation without using any LLM; full reliance on LLMs; and a hybrid mode, depending on the amount of application of the LLM in the preparation of the assignments. The proposed solution was evaluated quantitatively, with the aim of becoming a benchmark for examining the integration of LLM studies into higher education. Findings reveal the importance of hybrid mode, as the most preferred approach among students. 

Place, publisher, year, edition, pages
IEEE, 2024.
Keywords [en]
AI learning tool, case study, ChatGPT, large language models, higher education, practical implementation
National Category
Computer and Information Sciences Educational Sciences
Research subject
Computer and Information Sciences Computer Science
Identifiers
URN: urn:nbn:se:lnu:diva-131538DOI: 10.1109/EDUCON60312.2024.10578855Scopus ID: 2-s2.0-85207123960ISBN: 979-8-3503-9402-3 (electronic)OAI: oai:DiVA.org:lnu-131538DiVA, id: diva2:1885738
Conference
2024 IEEE Global Engineering EDUCATION CONFERENCE
Available from: 2024-07-25 Created: 2024-07-25 Last updated: 2025-02-11Bibliographically approved

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Zdravkova, KaterinaDalipi, FisnikAhlgren, FredrikIlijoski, BojanOhlsson, Tobias
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Department of InformaticsDigital TransformationsDepartment of computer science and media technology (CM)
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