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    <abstract>Large Language Models (LLMs) are increasingly used by students as informal assistants for problem solving and concept clarification. Despite growing adoption, the multilingual reliability of these systems for educational mathematics remains under-explored, particularly across Indian regional languages. In this work, we present a multilingual evaluation of contemporary LLMs on high-school mathematics problems across six Indian languages: English, Hindi, Bengali, Urdu, Gujarati, and Malayalam. To guide the design of lightweight prompting conditions, we conduct a small exploratory prompt elicitation exercise with Grade 11–12 students and observe that students predominantly use concise direct-solution requests and lightweight expertise-oriented phrasing. Guided by these observations, we evaluate GPT-4o-mini, LLaMA 3-8B, and DeepSeek-R1 using simplified student-inspired prompting strategies on a parallel corpus of numerically perturbed textbook mathematics problems. Our results reveal consistent multilingual performance disparities, with accuracy strongest in English and lower across several regional languages. We further analyze the effects of prompting strategy and mathematical topic, and provide qualitative error analysis identifying recurring arithmetic, reasoning, and regional-language comprehension failures. These findings highlight the importance of evaluating multilingual educational reliability as LLM-based learning assistance becomes increasingly accessible across diverse linguistic settings.</abstract>
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%0 Conference Proceedings
%T A Multilingual Evaluation of LLM-Based High-School Mathematics Assistance Across Indian Languages
%A Kumar, Yash
%A Roy, Subhajit
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-440-8
%F kumar-roy-2026-multilingual
%X Large Language Models (LLMs) are increasingly used by students as informal assistants for problem solving and concept clarification. Despite growing adoption, the multilingual reliability of these systems for educational mathematics remains under-explored, particularly across Indian regional languages. In this work, we present a multilingual evaluation of contemporary LLMs on high-school mathematics problems across six Indian languages: English, Hindi, Bengali, Urdu, Gujarati, and Malayalam. To guide the design of lightweight prompting conditions, we conduct a small exploratory prompt elicitation exercise with Grade 11–12 students and observe that students predominantly use concise direct-solution requests and lightweight expertise-oriented phrasing. Guided by these observations, we evaluate GPT-4o-mini, LLaMA 3-8B, and DeepSeek-R1 using simplified student-inspired prompting strategies on a parallel corpus of numerically perturbed textbook mathematics problems. Our results reveal consistent multilingual performance disparities, with accuracy strongest in English and lower across several regional languages. We further analyze the effects of prompting strategy and mathematical topic, and provide qualitative error analysis identifying recurring arithmetic, reasoning, and regional-language comprehension failures. These findings highlight the importance of evaluating multilingual educational reliability as LLM-based learning assistance becomes increasingly accessible across diverse linguistic settings.
%U https://aclanthology.org/2026.aacl-short.14/
%P 170-184

Markdown (Informal)

[A Multilingual Evaluation of LLM-Based High-School Mathematics Assistance Across Indian Languages](https://aclanthology.org/2026.aacl-short.14/) (Kumar & Roy, AACL-IJCNLP 2026)

• A Multilingual Evaluation of LLM-Based High-School Mathematics Assistance Across Indian Languages (Kumar & Roy, AACL-IJCNLP 2026)

ACL

• Yash Kumar and Subhajit Roy. 2026. A Multilingual Evaluation of LLM-Based High-School Mathematics Assistance Across Indian Languages. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 170–184, Hengqin, China. Association for Computational Linguistics.