Files and links (1)
Assessment integrity and validity in the teaching laboratory adapting to GenAI by developing an understanding of the verifiable learning objectives b7.61 MB
Published Version (Advanced Access)CC BY-NC-ND V4.0, Open Access
Journal article
Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection
European Journal of Engineering Education , Vol.50(4), pp.673-701
2025
Abstract
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 WollongongThomas Suesse - University of WollongongSarah Grundy - UNSW SydneyRezwanul Haque - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringSarah Lyden - University of TasmaniaSulakshana Lal - Curtin UniversityGhulam Mubashar Hassan - The University of Western AustraliaScott Arthur Daniel - University of Technology SydneyMarina 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; 0304-3797
- 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
Metrics
1 File views/ downloads
27 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Collaboration types
- Domestic collaboration
- Web Of Science research areas
- Education & Educational Research