Detecting AI-Generated Essays in Writing Assessment: Responsible Use and Generalizability Across LLMs
arXiv:2603.02353v1 Announce Type: new Abstract: Writing is a foundational literacy skill that underpins effective communication, fosters critical thinking, facilitates learning across disciplines, and enables individuals to organize and articulate complex ideas. Consequently, writing assessment plays a vital role in evaluating language proficiency, communicative effectiveness, and analytical reasoning. The rapid advancement of large language models (LLMs) has made it increasingly easy to generate coherent, high-quality essays, raising significant concerns about the authenticity of student-submitted work. This chapter first provides an overview of the current landscape of detectors for AI-generated and AI-assisted essays, along with guidelines for their responsible use. It then presents empirical analyses to evaluate how well detectors trained on essays from one LLM generalize to identifying essays produced by other LLMs, based on essays generated in response to public GRE writing prom
arXiv:2603.02353v1 Announce Type: new Abstract: Writing is a foundational literacy skill that underpins effective communication, fosters critical thinking, facilitates learning across disciplines, and enables individuals to organize and articulate complex ideas. Consequently, writing assessment plays a vital role in evaluating language proficiency, communicative effectiveness, and analytical reasoning. The rapid advancement of large language models (LLMs) has made it increasingly easy to generate coherent, high-quality essays, raising significant concerns about the authenticity of student-submitted work. This chapter first provides an overview of the current landscape of detectors for AI-generated and AI-assisted essays, along with guidelines for their responsible use. It then presents empirical analyses to evaluate how well detectors trained on essays from one LLM generalize to identifying essays produced by other LLMs, based on essays generated in response to public GRE writing prompts. These findings provide guidance for developing and retraining detectors for practical applications.
Executive Summary
This article critically examines the detection of AI-generated essays in writing assessment, addressing the growing concern of authenticity in student-submitted work. The authors provide an overview of current detectors for AI-generated essays and offer guidelines for their responsible use. They also present empirical analyses evaluating the generalizability of detectors trained on essays from one Large Language Model (LLM) to identifying essays produced by other LLMs. The findings of this study suggest that detectors may not generalize well across different LLMs, emphasizing the need for retraining and adaptation. This research contributes to the development of effective and responsible tools for detecting AI-generated essays in writing assessment, with significant implications for educational institutions and policymakers.
Key Points
- ▸ The article highlights the importance of writing assessment in evaluating language proficiency and communicative effectiveness.
- ▸ The rapid advancement of LLMs has led to concerns about the authenticity of student-submitted work.
- ▸ Detectors for AI-generated essays may not generalize well across different LLMs, necessitating retraining and adaptation.
Merits
Comprehensive Overview
The article provides a thorough review of current detectors for AI-generated essays, addressing their strengths and limitations.
Empirical Analysis
The authors present empirical findings evaluating the generalizability of detectors across different LLMs, adding depth to the discussion.
Practical Implications
The study's findings have significant implications for educational institutions and policymakers, emphasizing the need for responsible use of detectors.
Demerits
Limited Scope
The study focuses on essays generated in response to public GRE writing prompts, which may not be representative of all writing assessment scenarios.
Methodological Limitations
The empirical analyses may be limited by the sample size and selection of LLMs used in the study.
Expert Commentary
This article offers a nuanced examination of the detection of AI-generated essays, highlighting both the strengths and limitations of current detectors. The empirical analyses provide valuable insights into the challenges of generalizing detectors across different LLMs. However, the study's focus on a specific writing assessment scenario may limit its broader applicability. Nonetheless, the article's findings have significant implications for educational institutions and policymakers, underscoring the need for responsible and adaptive approaches to detecting AI-generated essays.
Recommendations
- ✓ Future research should investigate the development of more robust detectors that can effectively generalize across different LLMs.
- ✓ Educational institutions should establish clear guidelines for the use of detectors in writing assessment, prioritizing transparency and accountability.