# GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education

arXiv: https://arxiv.org/abs/2403.19148 · HTML: https://mlsanity.com/paper/2403.19148/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 71 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 18 of 18)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense](https://mlsanity.com/paper/2303.13408/) (2023)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://mlsanity.com/paper/2307.02477/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [PromptChainer: Chaining Large Language Model Prompts through Visual Programming](https://mlsanity.com/paper/2203.06566/) (2022)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models](https://mlsanity.com/paper/2305.13711/) (2023)
- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [A Comprehensive AI Policy Education Framework for University Teaching and Learning](https://mlsanity.com/paper/2305.00280/) (2023)
- [Game of Tones: Faculty detection of GPT-4 generated content in university assessments](https://mlsanity.com/paper/2305.18081/) (2023)
- [The AI Assessment Scale (AIAS): A Framework for Ethical Integration of Generative AI in Educational Assessment](https://mlsanity.com/paper/2312.07086/) (2023)
- [ChatGPT v Bard v Bing v Claude 2 v Aria v human-expert. How good are AI chatbots at scientific writing?](https://mlsanity.com/paper/2309.08636/) (2023)
- [The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment- A Preprint](https://mlsanity.com/paper/2403.14692/) (2024)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)

## Built on by (most cited first, 14 of 14)

- [AuthorMist: Evading AI Text Detectors with Reinforcement Learning](https://mlsanity.com/paper/2503.08716/) (2025)
- [GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challenge](https://mlsanity.com/paper/2412.18274/) (2024)
- [Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Media](https://mlsanity.com/paper/2404.15601/) (2024)
- [Comprehensive AI Assessment Framework: Enhancing Educational Evaluation with Ethical AI Integration](https://mlsanity.com/paper/2407.16887/) (2024)
- [Pangram 4 Technical Report](https://mlsanity.com/paper/2607.27183/) (2026)
- [Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback](https://mlsanity.com/paper/2406.15808/) (2024)
- [Generative AI Tools in Academic Research: Applications and Implications for Qualitative and Quantitative Research Methodologies](https://mlsanity.com/paper/2408.06872/) (2024)
- [Generative AI and Agency in Education: A Critical Scoping Review and Thematic Analysis](https://mlsanity.com/paper/2411.00631/) (2024)
- [The Statistical Signature of LLMs](https://mlsanity.com/paper/2602.18152/) (2026)
- [Paraphrasing Attack Resilience of Various AI-Generated Text Detection Methods](https://mlsanity.com/paper/2605.14240/) (2026)
- [A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models](https://mlsanity.com/paper/2503.15205/) (2025)
- [Gen AI in Proof-based Math Courses: A Pilot Study](https://mlsanity.com/paper/2509.13570/) (2025)
- [Humanly: A Configurable and Traceable Environment for Human-AI Collaborative Writing](https://mlsanity.com/paper/2607.21758/) (2026)
- [Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating](https://mlsanity.com/paper/2608.01112/) (2026)

## Abstract (arXiv)

This study investigates the efficacy of six major Generative AI (GenAI) text detectors when confronted with machine-generated content that has been modified using techniques designed to evade detection by these tools (n=805). The results demonstrate that the detectors' already low accuracy rates (39.5%) show major reductions in accuracy (17.4%) when faced with manipulated content, with some techniques proving more effective than others in evading detection. The accuracy limitations and the potential for false accusations demonstrate that these tools cannot currently be recommended for determining whether violations of academic integrity have occurred, underscoring the challenges educators face in maintaining inclusive and fair assessment practices. However, they may have a role in supporting student learning and maintaining academic integrity when used in a non-punitive manner. These results underscore the need for a combined approach to addressing the challenges posed by GenAI in academia to promote the responsible and equitable use of these emerging technologies. The study concludes that the current limitations of AI text detectors require a critical approach for any possible implementation in HE and highlight possible alternatives to AI assessment strategies.