GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challenge

TL;DR

This paper reviews the first Academic Essay Authenticity Challenge, highlighting advances in AI vs. human essay detection with high accuracy, driven by transformer models and LLMs across English and Arabic.

Contribution

It introduces a new benchmark dataset and evaluation framework for AI-generated essay detection, showcasing state-of-the-art results and diverse approaches from multiple teams.

Findings

Top systems achieved F1 scores over 0.98

Transformer-based models significantly outperformed baselines

Both English and Arabic detection tasks showed high accuracy

Abstract

This paper presents a comprehensive overview of the first edition of the Academic Essay Authenticity Challenge, organized as part of the GenAI Content Detection shared tasks collocated with COLING 2025. This challenge focuses on detecting machine-generated vs. human-authored essays for academic purposes. The task is defined as follows: "Given an essay, identify whether it is generated by a machine or authored by a human.'' The challenge involves two languages: English and Arabic. During the evaluation phase, 25 teams submitted systems for English and 21 teams for Arabic, reflecting substantial interest in the task. Finally, seven teams submitted system description papers. The majority of submissions utilized fine-tuned transformer-based models, with one team employing Large Language Models (LLMs) such as Llama 2 and Llama 3. This paper outlines the task formulation, details the dataset construction process, and explains the evaluation framework. Additionally, we present a summary of the approaches adopted by participating teams. Nearly all submitted systems outperformed the n-gram-based baseline, with the top-performing systems achieving F1 scores exceeding 0.98 for both languages, indicating significant progress in the detection of machine-generated text.