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Vision-Language Models Show Promise in Grading Handwritten Exams

By PulseAugur Editorial · [1 sources] ·

Researchers have evaluated the effectiveness of various vision-language models (VLMs) in grading handwritten examinations for outcome-based education. The study compared configurations including Qwen2.5-VL, InternVL3, and Pixtral, utilizing methods like zero-shot prompting, few-shot prompting, and Low-Rank Adaptation (LoRA). Qwen2.5-VL with LoRA achieved a Quadratic Weighted Kappa (QWK) of 0.727, surpassing the average human-pair QWK of 0.551, indicating potential for automated grading. However, the research also highlighted challenges such as mark variability across runs and a lack of consensus on the usefulness of model-generated explanations. AI

IMPACT These models show potential for automating the grading of handwritten exams, improving efficiency and consistency over manual methods.

RANK_REASON The cluster is based on an academic paper detailing research into the application of vision-language models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Vision-Language Models Show Promise in Grading Handwritten Exams

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The cluster is based on an academic paper detailing research into the application of vision-language models for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Md Khalid Syfullah, Asif Hasan Tonmoy, Saad Ahmed, S. M. Jahangir Alam ·

    Vision-Language Models for Criterion-Level Grading of Handwritten Examinations in Outcome-Based Education

    arXiv:2609.14284v1 Announce Type: new Abstract: Criterion-level grading connects examination performance to learning outcomes, but manual marking introduces workload and variation between markers. This study evaluates vision-language models (VLMs) for handwritten outcome-based as…

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