An AI-Based Adaptive Learning Platform for Multilingual and Low-Resource Educational Contexts: A Case Study on Nigeria

A new AI-based adaptive learning platform has been developed to address educational challenges in under-resourced and multilingual environments, specifically in Nigeria. The platform utilizes fine-tuned large language models within a personalized and adaptive learning (PAL) framework, focusing on linguistic inclusivity by incorporating Nigerian Pidgin English. This initiative aims to improve learner engagement and inclusivity by overcoming limitations such as inadequate personalization and language support in such contexts.

Why it matters

This development is crucial for expanding educational access and effectiveness in regions with linguistic diversity and limited resources. By leveraging AI for adaptive and personalized learning, it can foster greater engagement and improve learning outcomes for underserved populations. This approach offers a scalable model for addressing global educational disparities.

Key insights

• Educational platforms in under-resourced and multilingual contexts often suffer from limited personalization, insufficient language support, and weak curriculum internationalization.
• These limitations result in reduced learner engagement and inclusivity.
• An AI-based adaptive learning platform has been designed to tackle these specific issues.
• The platform integrates fine-tuned large language models (LLMs) within a Personalized and Adaptive Learning (PAL) framework.
• The system addresses linguistic inclusivity and computational constraints relevant to resource-limited environments.
• A case study on Nigerian Pidgin English demonstrates the platform's application.
• A curated Nigerian Pidgin corpus was developed to enhance linguistic alignment through LLM fine-tuning.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2608.15738