Mastering knowledge : The impact of generative AI on student learning outcomes

Journal article


Pallant, Jessica L., Blijlevens, Janneke, Campbell, Alexander and Jopp, Ryan. (2025). Mastering knowledge : The impact of generative AI on student learning outcomes. Studies in Higher Education. pp. 1-22. https://doi.org/10.1080/03075079.2025.2487570
AuthorsPallant, Jessica L., Blijlevens, Janneke, Campbell, Alexander and 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.

Keywordslearning outcomes; generative AI; higher education; goal structures; qualitative content analysis
Year2025
JournalStudies in Higher Education
Journal citationpp. 1-22
PublisherRoutledge
ISSN0307-5079
Digital Object Identifier (DOI)https://doi.org/10.1080/03075079.2025.2487570
Scopus EID2-s2.0-105004430611
Open accessPublished as ‘gold’ (paid) open access
Page range1-22
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online06 May 2025
Publication process dates
Accepted27 Mar 2025
Deposited13 Jun 2025
Additional information

© 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.

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