Rubric-Based Assessment of Student Reports Using Small Language Models

The paper presents a rubric‑based assessment pipeline that uses lightweight language models and a vision‑language model to evaluate student reports in cybersecurity education. It demonstrates that small models can achieve up to 97% accuracy on text‑only assessments and 94% on rich‑text assessments across various low‑resource hardware setups. The solution is open‑source, locally deployable, and designed to reduce instructor workload while addressing privacy and cost concerns.

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• llms
• multimodal
• safety