# Human-AI Collaboration or Academic Misconduct? Measuring AI Use in Student Writing Through Stylometric Evidence

arXiv: https://arxiv.org/abs/2505.08828 · HTML: https://mlsanity.com/paper/2505.08828/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 4 references that are in the map.
- First posted on arXiv 13 May 2025.

## Built on by (most cited first, 3 of 3)

- [Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference](https://mlsanity.com/paper/2508.04586/) (2025)
- [N-GLARE: An Non-Generative Latent Representation-Efficient LLM Safety Evaluator](https://mlsanity.com/paper/2511.14195/) (2025)
- [Serial-Parallel Dual-Path Architecture for Speaking Style Recognition](https://mlsanity.com/paper/2510.11732/) (2025)

## Abstract (arXiv)

As human-AI collaboration becomes increasingly prevalent in educational contexts, understanding and measuring the extent and nature of such interactions pose significant challenges. This research investigates the use of authorship verification (AV) techniques not as a punitive measure, but as a means to quantify AI assistance in academic writing, with a focus on promoting transparency, interpretability, and student development. Building on prior work, we structured our investigation into three stages: dataset selection and expansion, AV method development, and systematic evaluation. Using three datasets - including a public dataset (PAN-14) and two from University of Melbourne students from various courses - we expanded the data to include LLM-generated texts, totalling 1,889 documents and 540 authorship problems from 506 students. We developed an adapted Feature Vector Difference AV methodology to construct robust academic writing profiles for students, designed to capture meaningful, individual characteristics of their writing. The method's effectiveness was evaluated across multiple scenarios, including distinguishing between student-authored and LLM-generated texts and testing resilience against LLMs' attempts to mimic student writing styles. Results demonstrate the enhanced AV classifier's ability to identify stylometric discrepancies and measure human-AI collaboration at word and sentence levels while providing educators with a transparent tool to support academic integrity investigations. This work advances AV technology, offering actionable insights into the dynamics of academic writing in an AI-driven era.