Work

Are Large Language Models for Education Reliable Across Languages?

Year: 2025

Type: conference-paper

Abstract: Large language models (LLMs) are increasingly being adopted in educational settings.These applications expand beyond English, though current LLMs remain primarily Englishcentric.In this work, we ascertain if their use in education settings in non-English languages is warranted.We evaluated the performance of popular LLMs on four educational tasks: identifying student misconceptions, providing targeted feedback, interactive tutoring, and grading translations in eight languages (Mandarin, Hindi, Arabic, German, Farsi, Telugu, Ukrainian, Czech) in addition to English.We find that the performance on these tasks somewhat corresponds to the amount of language represented in training data, with lowerresource languages having poorer task performance.However, at least some models are able to more or less maintain their levels of performance across all languages.Thus, we recommend that practitioners first verify that the LLM works well in the target language for their educational task before deployment.

Source: Workshop on Innovative Use of NLP for Building Educational Applications

DOI: 10.18653/v1/2025.bea-1.44

Authors: Vansh Gupta, Sankalan Pal Chowdhury, Vilém Zouhar, Donya Rooein, Mrinmaya Sachan

Institutions: ETH Zurich, Bocconi University

Language: English

Topic: Intelligent Tutoring Systems and Adaptive Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: hybrid

Funders: European Commission, École Polytechnique Fédérale de Lausanne, H2020 European Research Council