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Cited by 27Academic integrity and plagiarismStudent Assessment and FeedbackArtificial Intelligence in Education

Redefining assessment tasks to promote students’ creativity and integrity in the age of generative artificial intelligence

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Peters, Martine, Angelov, Dimitar·2025·International Journal for Educational Integrity
DOI 10.1007/s40979-025-00201-xPMC PMC12466509
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Abstract

The arrival of generative artificial intelligence (GenAI) has forced lecturers to adjust their assessment practices to ensure that students’ work is their own from a creative point of view, and free of plagiarism. This chapter proposes the Academic Integrity and Creativity in the Age of Artificial Intelligence (AICAI) model for the use of authentic assessment as a possible strategy to promote students’ creativity and integrity and thereby ensure the ownership of their written work. Lecturers are encouraged to rethink the assignments they design and examine each of the following components with an eye to integrity: their professional characteristics, the objectives for the assignment, the type of assessment that is appropriate for the needs of the student. Others include the cognitive offloading that will be done or not with GenAI, the type of authentic task they wish to propose and its characteristics, and the instructions and criteria that will be given to students. The choices made should engage students, thereby diminishing the temptation to plagiarize. By combining different strands of pedagogical theory and research, the AICAI assessment design model proposed in this paper has brought into focus the challenges as well as the opportunities that have emerged with the inclusion of GenAI in higher education. On a more practical level, it offers a systemic approach and advice as to how the challenges can be mitigated and benefits maximized for all parties involved in assessment.

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  • Evaluating unseen and situated evidence diminishes the temptation to plagiarize and prevents cognitive offloading to AI.

    “This diminishes the temptation to plagiarize and prevents cognitive offloading to AI (Peters 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 Peters et al., 2025

    The 'Authentic assessment tasks' section (page unavailable) describes authentic assessment as a deterrent to plagiarism, while noting that it does not always prevent cheating. The 'Cognitive offloading' section (page unavailable) advocates deciding when and how students may use GenAI for offloading, not preventing it. The copy therefore supports the plagiarism-deterrence part, but not the claim that evaluating unseen and situated evidence prevents cognitive offloading to AI. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Peters, M., & Angelov, D.
Affiliations
  • Université du Québec en Outaouais (CA)
  • Coventry University (GB)
Corresponding author
  • Peters, Martine
  • Angelov, Dimitar
ORCID
Venue
International Journal for Educational Integrity
Publisher
Springer Science and Business Media LLC
Published
2025-09-23
Volume
21
Issue
1
Pages
25-25
ISSN
1833-2595 (ISSN-L)
DOI
10.1007/s40979-025-00201-x
PMC
PMC12466509
OpenAlex
W4414397238
Cited by
27
References
20
Citations by year
2026: 26 · 2025: 1
FWCI
19.58
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal for Educational Integrity (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • Pure (Coventry University) (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Social Sciences and Humanities Research Council of Canada (895-2021-1016)
Topics
  • Social Sciences › Safety Research › Academic integrity and plagiarism
  • Social Sciences › Education › Student Assessment and Feedback
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
Keywords
generative AI, academic integrity, student creativity, assessment design, authentic assessment, plagiarism avoidance, cognitive offloading, assignment design, higher education assessment

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