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Cited by 9Artificial Intelligence in EducationAcademic integrity and plagiarismStudent Assessment and Feedback

Thinking to learn: managing the risks of outsourcing to GenAI

Open access
Chase, Anne-Marie, Galvin, Kelly·2026·Assessment & Evaluation in Higher Education
DOI 10.1080/02602938.2026.2620055
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Abstract

This conceptual paper addresses the pedagogical and ethical challenges posed by generative artificial intelligence (GenAI) in higher education assessment. While GenAI can enhance productivity and innovation, it introduces challenges related to cognitive offloading, academic integrity and assessment validity. Responding to sector-wide calls for structural reform, this paper proposes a developmental assessment framework that enables educators to make principled decisions about GenAI use. Drawing on recent scholarship, it synthesises key concerns, cognitive offloading, ethical boundaries, pre-existing assessment challenges and design frameworks, to establish the pedagogical and regulatory foundations for assessment transformation. The paper’s contribution is twofold: it applies Griffin’s developmental assessment model to GenAI contexts, and it combines that model with the AI Assessment Scale (AIAS) to operationalise principled decisions about where GenAI may ethically support assessment without compromising validity. The framework guides educators through aligning learning outcomes with assessment tasks, unpacking constructs, identifying capabilities and mapping observable indicators to AI use. This structured approach complements tools such as the AI Assessment Scale (AIAS) by embedding validity and cognitive ownership into assessment design. It supports transparent, defensible decision-making and shifts the focus from rule enforcement to thoughtful design. Implications for educators, students, institutions, and integrity and governance are explored, with attention to implementation and the need for sustained development. In doing so, this paper reframes assessment integrity as a design challenge rather than a compliance issue, contributing to a more principled and inclusive approach to learning in the GenAI era.

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Crossref · 10.1080/02602938.2026.2620055

Cites

31 of 31 references verified0 citation errors · 0 not checkable

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  • Cognitive offloading is not inherently maladaptive; some forms of strategic offloading may reduce unnecessary load, free up cognitive resources, and support higher-order thinking, but the same process may become problematic when learners rely on GenAI without verification, without reflection, or without maintaining ownership of the task.

    “Importantly, the literature suggests that offloading is not inherently maladaptive. Some forms of strategic offloading may reduce unnecessary load, free up cognitive resources, and support higher-order thinking. However, the same process may also become problematic when learners rely on GenAI without verification, without reflection, or without maintaining ownership of the task”

    From Generative AI, Cognitive Offloading, and Learner Agency in Higher Education: A Scoping Review (2026) · cites this paper as Chase & Galvin, 2026

  • Unguided use of AI-generated content may contribute to homogenized responses, reduced argumentative diversity, and more monologic forms of participation, particularly when students reproduce algorithmic outputs without reinterpretation or critique.

    “Research suggests that unguided use of AI-generated content may contribute to homogenized responses, reduced argumentative diversity, and more monologic forms of participation, particularly when students reproduce algorithmic outputs without reinterpretation or critique (Chase and Galvin, 2026;Susena et al., 2026).”

    From Between cognitive offloading and critical autonomy: A systematic review of the epistemic implications of generative AI in higher education. (2026) · cites this paper as Chase and Galvin, 2026

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Cites

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Research works printed but not linked: 6

  1. Department of Education. 2021. Higher Education Standards Framework (Threshold Standards). Canberra, Australia: Tertiary Education Quality and Standards Agency.
  2. Gallant, T. B., and D. A. Rettinger. 2025. The Opposite of Cheating: Teaching for Integrity in the Age of AI. Vol. 4. Norman, OK: University of Oklahoma Press.
  3. 10.1017/9781108116053 · DOI
  4. Grove, J. 2025. Permanent ‘Training Wheels’ of AI ‘Inhibits Student Learning’. Times Higher Education. https://www.timeshighereducation.com/news/permanent-training-wheels-ai-inhibit-student-learning.
  5. McTighe, J., and T. Frontier. 2022. “How to Provide Better Feedback through Rubrics.” Educational Leadership 79 (7): 17–23.
  6. Turner, G. 2025. Broken: Universities, Politics and the Public Good. Clayton, VIC, Australia: Monash University Publishing.

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Authors
Chase, A.-M., & Galvin, K.
Affiliations
Swinburne University of Technology (AU)
Corresponding author
Chase, Anne-Marie
ORCID
Venue
Assessment & Evaluation in Higher Education
Publisher
Informa UK Limited
Published
2026-01-27
Pages
1-20
ISSN
  • 0260-2938 (ISSN-L)
  • 1469-297X
DOI
10.1080/02602938.2026.2620055
OpenAlex
W7125916683
Semantic Scholar
37f3a98c763df2fe4eaa46298839c9588003175d
Cited by
8
References
30
Citations by year
2026: 8
FWCI
36.35
Type
journal article
Language
en
Open access
Licence
Locations
Assessment & Evaluation in Higher Education (publishedVersion, cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Education › Student Assessment and Feedback
Keywords
generative AI, higher education assessment, academic integrity, assessment validity, cognitive offloading, assessment design, AI ethics in education, learning outcomes alignment

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