Assessing Large Language Models in Children's Education in Low-Resource Settings: Opportunities and Challenges
Abstract
Large language models (LLMs) offer scalable and adaptive learning solutions for children's education in low-resource settings. While they enhance engagement and accessibility, challenges such as bias, privacy risks, and infrastructure limitations remain. This paper reviews and highlights key issues and proposes strategies to ensure equitable and effective AI-driven education.
Cite
Text
Mienye et al. "Assessing Large Language Models in Children's Education in Low-Resource Settings: Opportunities and Challenges." ICLR 2025 Workshops: AI4CHL, 2025.Markdown
[Mienye et al. "Assessing Large Language Models in Children's Education in Low-Resource Settings: Opportunities and Challenges." ICLR 2025 Workshops: AI4CHL, 2025.](https://mlanthology.org/iclrw/2025/mienye2025iclrw-assessing/)BibTeX
@inproceedings{mienye2025iclrw-assessing,
title = {{Assessing Large Language Models in Children's Education in Low-Resource Settings: Opportunities and Challenges}},
author = {Mienye, Ibomoiye Domor and Obaido, George Rabeshi and Obi, Thabisile},
booktitle = {ICLR 2025 Workshops: AI4CHL},
year = {2025},
url = {https://mlanthology.org/iclrw/2025/mienye2025iclrw-assessing/}
}