Reliability, validity, and diagnostic evidence for multi-model LLM short-answer scoring
Large language models (LLMs) are increasingly used or proposed for educational scoring, but single-model and single-run evaluations provide limited evidence for assessment use. Short-answer scoring requires evidence about reliability, validity, severity, diagnostic value, and failure cases. This study evaluated repeated multi-model OCG-PRES guided LLM scoring for short-answer assessment. The analysis used 996 SciEntsBank responses. GPT, DeepSeek, and Qianwen each scored every response across three independent runs using five OCG-PRES dimensions: concept coverage, relation accuracy, reasoning completeness, contradiction control, and domain relevance. Scores were evaluated against official ...
| Authors | Chunyi Zhao, Chao Li |
|---|---|
| Primary category | cs.CL |
| Published | 6 September 2026 |
| Last updated at source | 6 September 2026 |
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