Preprint
Exploring LLM Autoscoring Reliability in Large-Scale Writing Assessments Using Generalizability Theory
ArXiv.org
Cornell University
07/29/2025
DOI: 10.48550/arxiv.2507.19980
Abstract
This study investigates the estimation of reliability for large language models (LLMs) in scoring writing tasks from the AP Chinese Language and Culture Exam. Using generalizability theory, the research evaluates and compares score consistency between human and AI raters across two types of AP Chinese free-response writing tasks: story narration and email response. These essays were independently scored by two trained human raters and seven AI raters. Each essay received four scores: one holistic score and three analytic scores corresponding to the domains of task completion, delivery, and language use. Results indicate that although human raters produced more reliable scores overall, LLMs demonstrated reasonable consistency under certain conditions, particularly for story narration tasks. Composite scoring that incorporates both human and AI raters improved reliability, which supports that hybrid scoring models may offer benefits for large-scale writing assessments.
Details
- Title: Subtitle
- Exploring LLM Autoscoring Reliability in Large-Scale Writing Assessments Using Generalizability Theory
- Creators
- Dan Song - University of IowaWon-Chan Lee - University of IowaHong Jiao - University of Maryland, Baltimore
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2507.19980
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 07/29/2025
- Academic Unit
- Psychological and Quantitative Foundations
- Record Identifier
- 9984927210602771
Metrics
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