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Journal article
Exploring the Secondary Indicators of Generative AI in Academic Misconduct
Annals of Nursing and Practice, Vol.12(1), pp.1-6
2025
Abstract
Background: The increasing inappropriate use of GenAI tools has seen an increase in referrals to the Integrity and Compliance Unit of a regional
university. There is no clear definitive characteristic that indicates this inappropriate use.
Aim: This audit of referrals to the Integrity and Compliance Unit aims to identify secondary characteristics commonly seen in students’ assessments that indicate a GenAI tool may have been used.
Design: A retrospective descriptive audit of anonymised student assessments referred to the Integrity Compliance Unit for investigation for potential academic misconduct.
Methods: A retrospective audit of anonymised student assessments was undertaken, looking for secondary characteristics of GenAI tool use. Each author reviewed and agreed to the characteristics identified.
Results: The secondary characteristics commonly identified related to formatting, metadata, references and citations, and terminology and language. No one characteristic was definitive in confirming the use of GenAI.
Conclusions: No one characteristic can be used to define if GenAI has been used in an assessment. However, the greater the number of secondary
characteristics identified, the higher the confidence an investigator can have that on the balance of probabilities a GenAI tool has been used. Using the secondary characteristics highlighted in this paper as a guide, investigators can be confident in identifying if GenAI has been used or not.
Details
- Title
- Exploring the Secondary Indicators of Generative AI in Academic Misconduct
- Authors
- Anthony Summers (Corresponding Author) - University of the Sunshine Coast, Queensland, School of Health - NursingThea Vanags (Author) - University of the Sunshine Coast, Queensland, Centre for Support and Advancement of Learning and Teaching
- Publication details
- Annals of Nursing and Practice, Vol.12(1), pp.1-6
- Publisher
- JSciMed Central
- Date published
- 2025
- ISSN
- 2379-9501
- Copyright note
- © 2025 Summers A, et al.
- Organisation Unit
- School of Health - Nursing
- Language
- English
- Record Identifier
- 991170145802621
- Output Type
- Journal article
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