Semantic Grading of Written Answers in Low-Resource Language Bangla Using a Fine-Tuned Lightweight Language Model
arXiv:2606.11931v1 Announce Type: new Abstract: Bangla is among the world's most widely spoken languages, yet it remains underserved in educational NLP research. In many remote and rural regions, access to qualified subject teachers is limited, and written answers are consequently graded largely by hand, restricting timely and consistent feedback. Automatic assessment is challenging because semantically correct responses can vary substantially in surface form. We present a bilingual (Bangla-English) evaluation system designed for low-resource educational settings that prioritizes semantic corr
The increasing availability of lightweight language models and the growing focus on AI for education in diverse linguistic contexts make this development timely.
This development addresses a critical need for scalable educational tools in low-resource languages, potentially democratizing access to quality feedback and learning.
The ability to semantically grade answers in languages like Bangla using AI models opens new avenues for automated assessment beyond widely-resourced languages.
- · Bangla-speaking students and educators
- · Educational technology providers
- · AI developers specializing in low-resource languages
- · Traditional manual grading systems
- · EdTech platforms focused only on high-resource languages
Improved educational outcomes and reduced teacher workload in regions using the system.
Increased demand for AI models and datasets for other low-resource languages, fostering linguistic diversity in AI.
Potential for new economic opportunities in educational content and tools tailored to specific linguistic and cultural contexts.
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