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Cited by 6Academic integrity and plagiarismArtificial Intelligence in EducationStudent Assessment and Feedback

Work in Progress: Safeguarding Authenticity: Strategies for Combating AI-Generated Plagiarism in Academia

Ilic, Peter, Carr, Nicholas·2023·2023 IEEE Frontiers in Education Conference (FIE)
DOI 10.1109/fie58773.2023.10343337
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Abstract

This work-in-progress (WiP) research explores the role of rubrics in mitigating the negative impact of generative AI, such as ChatGPT, on writing assessment practices in STEM. This approach addresses the growing need for innovative methods of ensuring student academic integrity and authenticity in the rapidly expanding ecosystem of AI tools. A rubric consisting of five criteria is employed to rate the students' deconstruction of written text into language frames for the purpose of differentiating between human-written and AI-generated content. The language frames are common English language sentence patterns used for expressing five academic written functions: compare/contrast, cause/effect, classification, chronological order, and spatial order. By evaluating the performance of student deconstruction of one paragraph written by the student and a second paragraph produced by ChatGPT, it is anticipated that the rubric will enable the instructor to differentiate between the two by capturing any gaps in knowledge required to identify, deconstruct, and reproduce previously learned sentence frames. This assumes that the student will be more familiar with a self-written text than an unfamiliar AI produced one. This difference may then be employed by educators to aid in the identification of AI-generated plagiarism submitted by students. The key insights from this pilot study include: The need for a rubric threshold level of between 70% and 80% to differentiate between human and AI texts. Students appear to score higher at the identification of language frames than the production of the same frames. They were equal or better at identifying sentence frames from the AI generated text. Also, students scored very low on critical thinking questions that required the selection of alternative sentence frames. This WiP paper details the rubric design, research methodology, and preliminary insights from a small pilot study, which informs the evolution towards a larger future implementation.

Integrity signals

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Crossref · 10.1109/fie58773.2023.10343337

Cites

23 of 23 references verified0 citation errors · 0 not checkable

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This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Ilic and Carr applied an innovative rubric to distinguish between human-written and AI-generated text by evaluating students' ability to deconstruct and reconstruct academic language frames.

    “One such example of this is a study in which Ilic and Carr [34] applied an innovative rubric to distinguish between human-written and AI-generated text. It did this by evaluating students' ability to deconstruct and reconstruct academic language frames.”

    From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [34]

  • The results of Ilic and Carr's study were mixed, prompting further revision and testing before standardized implementation.

    “However, the study results were mixed, prompting further revision and testing before standardized implementation.”

    From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [34]

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Authors
Ilic, P., & Carr, N.
Affiliations
University of Aizu (JP)
ORCID
Venue
2023 IEEE Frontiers in Education Conference (FIE)
Publisher
IEEE
Published
2023-10-18
Pages
1-5
ISSN
0190-5848 (ISSN-L)
DOI
10.1109/fie58773.2023.10343337
OpenAlex
W4390607032
Cited by
6
References
23
Citations by year
2025: 4 · 2024: 2
FWCI
1.50
Type
conference paper
Language
en
Open access
closed
Licence
Locations
IEEE Frontiers in Education Conference (FIE) (publishedVersion)
Funders
Japan Society for the Promotion of Science (20K00862)
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, ChatGPT, academic integrity, rubrics, writing assessment, STEM education, sentence patterns, critical thinking, academic writing

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