# Contra generative AI detection in higher education assessments

arXiv: https://arxiv.org/abs/2312.05241 · HTML: https://mlsanity.com/paper/2312.05241/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2023.

## Built on by (most cited first, 4 of 4)

- [Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods](https://mlsanity.com/paper/2406.15583/) (2024)
- [Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse](https://mlsanity.com/paper/2506.16412/) (2025)
- [Show, Don't TELL: Explainable AI-Generated Text Detection](https://mlsanity.com/paper/2605.27921/) (2026)
- [LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators](https://mlsanity.com/paper/2606.29437/) (2026)

## 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.