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The growing integration of artificial intelligence (AI) in academic writing is reshaping research and composition, offering both opportunities and challenges. While AI tools enhance accessibility, concerns over academic integrity persist as AI-generated texts become increasingly difficult to distinguish from human writing. Institutions have implemented AI detection tools such as Turnitin, Originality.ai, and GPTZero, yet their accuracy remains inconsistent, and humanizing techniques further undermine their reliability. Given these limitations, relying solely on detection-based assessments is not a sustainable approach. Rather than prohibiting AI, academic evaluation could benefit from approaches that emphasize creativity, originality, and responsible AI integration. This study examines AI's role in student writing, critiques detection tools’ limitations, and proposes a process-based assessment framework that integrates AI interaction tracking, reflective analysis, and iterative revisions. By shifting from detection-focused models to AI-conscious evaluation, this research aims to establish a fairer and more effective approach to maintaining academic integrity.

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Detecting the Undetectable: The Need for a New Paradigm for Academic Writing Evaluation in the AI Era – Addressing Inconsistencies in AI and Plagiarism Detection Tools –

  • 会议论文

  • 发表日期：

    2025年

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  • Hat Nim Kim

摘要

The growing integration of artificial intelligence (AI) in academic writing is reshaping research and composition, offering both opportunities and challenges. While AI tools enhance accessibility, concerns over academic integrity persist as AI-generated texts become increasingly difficult to distinguish from human writing. Institutions have implemented AI detection tools such as Turnitin, Originality.ai, and GPTZero, yet their accuracy remains inconsistent, and humanizing techniques further undermine their reliability. Given these limitations, relying solely on detection-based assessments is not a sustainable approach. Rather than prohibiting AI, academic evaluation could benefit from approaches that emphasize creativity, originality, and responsible AI integration. This study examines AI's role in student writing, critiques detection tools’ limitations, and proposes a process-based assessment framework that integrates AI interaction tracking, reflective analysis, and iterative revisions. By shifting from detection-focused models to AI-conscious evaluation, this research aims to establish a fairer and more effective approach to maintaining academic integrity.