Appraising higher education assessment validity: Development of the PANDORA GenAI Susceptibility Rubric
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This paper presents the development and application of the PANDORA GenAI Susceptibility Rubric, a novel tool designed to assess the susceptibility of higher education assessments to the undeclared use of generative artificial intelligence (GenAI) tools. In response to growing concerns about academic integrity and the rising sophistication of GenAI technologies, the rubric provides educators with a structured framework to critically evaluate the validity of their assessments across key criteria, including collaborative authorship, intellectual task complexity, and the opportunity for creativity. Through a mixed-methods design, the rubric was refined to include expert-informed modifications and validated through end-user application across various arts and humanities courses. Results highlight how assessment design can either mitigate or exacerbate GenAI susceptibility, revealing that tasks requiring genuine collaboration, creative thinking, and process-oriented evaluation offer greater resistance to AI manipulation. The rubric also emphasises balancing detailed guidance with student autonomy to avoid facilitating GenAI prompt formulation. This study contributes to the field by offering a practical instrument that promotes more robust, ethically sound, and future-proofed assessment practices. It serves as a critical response to the pressing challenges posed by GenAI in higher education and informs ongoing discourse on academic integrity.
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Tasks requiring genuine collaboration offer greater resistance to AI manipulation.
“Tasks requiring genuine collaboration offer greater resistance to AI manipulation (Bannister et al., 2025).”
From OTE Reconstructing the Assessment System of the Undergraduate Course “Plant Physiology” in the AI Era: A Framework for Authentic and AI-Resilient Task Design (2026) · cites this paper as Bannister et al., 2025
Balancing detailed guidance with student autonomy helps avoid inadvertently facilitating GenAI prompt formulation.
“Furthermore, balancing detailed guidance with student autonomy helps avoid inadvertently facilitating GenAI prompt formulation (Bannister et al., 2025).”
From OTE Reconstructing the Assessment System of the Undergraduate Course “Plant Physiology” in the AI Era: A Framework for Authentic and AI-Resilient Task Design (2026) · cites this paper as Bannister et al., 2025
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- Authors
- Bannister, P., Urbieta, A. S., & Alvira, N. B.
- Affiliations
- Universidad Internacional de La Rioja (ES)
- Venue
- Journal of Applied Learning & Teaching
- Publisher
- Kaplan Business School, Australia
- Published
- 2025-01-23
- Volume
- 8
- Issue
- 1
- ISSN
- 2591-801X (ISSN-L)
- OpenAlex
- W4406774315
- Cited by
- 7
- References
- 83
- Citations by year
- 2026: 6 · 2025: 1
- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Applied Learning & Teaching (publishedVersion, cc-by)
- Re-Unir (International University of La Rioja) (submittedVersion, cc-by)
- Funders
- Universidad Internacional de La Rioja
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Student Assessment and Feedback
- Keywords
- generative AI, assessment validity, academic integrity, assessment design, collaborative authorship, student independence, prompt formulation, arts and humanities education, higher education assessment
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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