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Generative AI may impact students marks a case study from a large first-year statistics course2.00 MB
Published Version (Advanced Access)CC BY-NC-ND V4.0, Open Access
Journal article
Generative AI may impact students' marks: a case study from a large first-year statistics course
International Journal of Mathematical Education in Science and Technology, Vol.Advanced access
2025
Appears in UniSC Supported Open Access Outputs
Abstract
The arrival of generative AI (GenAI) near the end of 2022 impacted higher education in ways still being understood. In this study, marks and grades for students in a large first-year course are examined before the arrival of GenAI (for two offerings in 2022) and after the arrival of GenAI (two offerings in each of 2023 and 2024). The course itself, and the profile of the students studying the course, have remained very similar from 2022 to 2024, so any observed changes are likely to be a result of the emergence of GenAI. We found that GenAI potentially had a minimal impact on some assessment types (online quizzes; an assignment with guidelines requiring creativity), but a substantial and significant potential impact on other assessment types, some beneficially and some detrimentally. While pass rates in the course have remained relatively constant, the distribution of passing grades has also shifted since the introduction of GenAI. In addition, the introduction of GenAI has enhanced overall marks for passing students but reduced overall marks for failing students.
Details
- Title
- Generative AI may impact students' marks: a case study from a large first-year statistics course
- Authors
- Peter K. Dunn (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
- Publication details
- International Journal of Mathematical Education in Science and Technology, Vol.Advanced access
- Publisher
- Taylor & Francis
- DOI
- 10.1080/0020739X.2025.2539711
- ISSN
- 1464-5211; 0020-739X
- Copyright note
- © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
- Data Availability
- Data are not available as they contain the marks of individual students, which could lead to identification. The university Ethics Committee has deemed that the data should not be made available.
- Organisation Unit
- School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 991166461602621
- Output Type
- Journal article
Metrics
12 Record Views
InCites Highlights
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- Web Of Science research areas
- Education & Educational Research