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Mastering knowledge : The impact of generative AI on student learning outcomes
Pallant, Jessica L. Blijlevens, Janneke Campbell, Alexander Jopp, Ryan
Pallant, Jessica L.
Blijlevens, Janneke
Campbell, Alexander
Jopp, Ryan
Abstract
Generative AI (GenAI) has had a significant impact across industries since the launch of ChatGPT in late 2022. Much of the focus of existing research in the higher education space has considered the impact GenAI has had on academics and institutions. Conversely, research has been less focused on the impact this technology will have on students. Our research investigates how GenAI impacts student learning outcomes in higher education. We applied a quasi-experimental lens to analyse qualitative data of 192 student reflections and apply quantitative content analysis (QCA). Results indicate that a higher level of learning occurs when students use GenAI to construct and augment knowledge (mastery approach). In contrast, lower-level learning outcomes resulted from using GenAI procedurally without augmenting knowledge (procedural approach). Through a practical lens, the course curriculum can be designed to include GenAI to scaffold students’ learning from basic knowledge construction tasks to more complex augmentation of knowledge. Assessment design can be adjusted to promote mastery goal structures, encouraging students to critically engage with GenAI outputs rather than simply reproducing them, fostering optimal learning outcomes.
Keywords
learning outcomes, generative AI, higher education, goal structures, qualitative content analysis
Date
2025
Type
Journal article
Journal
Studies in Higher Education
Book
Volume
Issue
Page Range
1-22
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY-NC-ND 4.0
File Access
Open
Notes
© 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.