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

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.

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