Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection

Sasha Nikolic; Thomas Suesse; Sarah Grundy; Rezwanul Haque; Sarah Lyden; Sulakshana Lal; Ghulam Mubashar Hassan; Scott Arthur Daniel; Marina Belkina

doi:10.1080/03043797.2025.2456944

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Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection

Journal article

Open access

Peer reviewed

Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection

Sasha Nikolic, Thomas Suesse, Sarah Grundy, Rezwanul Haque, Sarah Lyden, Sulakshana Lal, Ghulam Mubashar Hassan, Scott Arthur Daniel and Marina Belkina

European Journal of Engineering Education , Vol.50(4), pp.673-701

2025

DOI: https://doi.org/10.1080/03043797.2025.2456944

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Abstract

assessment integrity

blooms taxonomy

generative artificial intelligence (GenAI)

laboratory

laboratory learning objectives measurement (LLOM)

Generative Artificial Intelligence (GenAI), such as ChatGPT, is reshaping educational paradigms by offering unparalleled benefits and introducing challenges, particularly academic integrity. This study investigates teaching laboratory practices (traditional, recorded, remote, simulation and virtual), considered an academic safe haven due to its authenticity, and examines how assessments align with learning objectives. This should reinvigorate interest in expanding laboratory learning opportunities. However, unsupervised laboratory reports, a dominant assessment type, present significant cheating risks – intensified by GenAI. Given the scant literature on laboratory assessments and their primary focus on cognitive objectives, little guidance is available regarding how to assess non-cognitive objectives. This studies innovative approach utilises a reflective survey with 134 international academic staff to explore how each assessment type can verify cognitive, psychomotor, and affective learning objectives. We introduce a ‘Words of Estimative Probability’ heatmap to visualise the likelihood of verifying specific learning objectives, providing a snapshot to guide academics in holistic assessment design. This study advocates for diverse assessments, which mitigate GenAI risks and foster comprehensive skill development. This research equips educators to design secure, effective laboratory education in STEM disciplines, ensuring alignment with evolving academic and technological landscapes by offering a framework for improving assessment validity, integrity, and adaptability.

Details

Title: Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection
Authors: Sasha Nikolic (Corresponding Author) - University of Wollongong
Thomas Suesse - University of Wollongong
Sarah Grundy - UNSW Sydney
Rezwanul Haque - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Sarah Lyden - University of Tasmania
Sulakshana Lal - Curtin University
Ghulam Mubashar Hassan - The University of Western Australia
Scott Arthur Daniel - University of Technology Sydney
Marina Belkina - Western Sydney University
Publication details: European Journal of Engineering Education , Vol.50(4), pp.673-701
Publisher: Taylor & Francis
Date published: 2025
DOI: 10.1080/03043797.2025.2456944
ISSN: 1469-5898
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.
Organisation Unit: School of Science, Technology and Engineering
Language: English
Record Identifier: 991098745802621
Output Type: Journal article

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Collaboration types: Domestic collaboration
Web Of Science research areas: Education & Educational Research