Contra generative AI detection in higher education assessments
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Abstract (arXiv)
This paper presents a critical analysis of generative Artificial Intelligence (AI) detection tools in higher education assessments. The rapid advancement and widespread adoption of generative AI, particularly in education, necessitates a reevaluation of traditional academic integrity mechanisms. We explore the effectiveness, vulnerabilities, and ethical implications of AI detection tools in the context of preserving academic integrity. Our study synthesises insights from various case studies, newspaper articles, and student testimonies to scrutinise the practical and philosophical challenges associated with AI detection. We argue that the reliance on detection mechanisms is misaligned with the educational landscape, where AI plays an increasingly widespread role. This paper advocates for a strategic shift towards robust assessment methods and educational policies that embrace generative AI usage while ensuring academic integrity and authenticity in assessments.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Dec 2023.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 2.
4 papers.
- 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methodscited by 23
- 2025Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discoursecited by 0
- 2026Show, Don't TELL: Explainable AI-Generated Text Detectioncited by 0
- 2026LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicatorscited by 0
- 2026LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators28 Jun 2026
- 2026Show, Don't TELL: Explainable AI-Generated Text Detection27 May 2026
- 2025Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse19 Jun 2025
- 2024Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods21 Jun 2024