Problem
Research questions and friction points this paper is trying to address.
Low-resource educational contexts
Multilingual education
Personalised and adaptive learning
Linguistic inclusivity
Nigerian Pidgin English
An AI-Based Adaptive Learning Platform for Multilingual and Low-Resource Educational Contexts: A Case Study on Nigeria
🤖 AI Summary
This study addresses the scarcity of personalized learning and linguistic support in low-resource multilingual education by developing an AI-adaptive platform for Nigerian Pidgin. Through instruction tuning and multi-level quantization of large language models, we propose an experimentally validated adaptation framework for low-resource languages, introducing a novel dual-validation mechanism combining native-speaker cultural assessment with automated metrics. Our research elucidates the trade-offs between quantization bit-width, semantic quality, and inference latency, demonstrating that this approach significantly reduces computational overhead with minimal pedagogical degradation. Ultimately, this work enables the scalable deployment of intelligent educational systems that effectively balance cultural appropriateness with computational feasibility in resource-constrained settings.
📝 Abstract
Educational platforms in under-resourced and multilingual contexts, such as Nigeria, often struggle with limited personalisation, inadequate language support, and weak curriculum internationalisation, leading to reduced learner engagement and inclusivity. This paper presents an AI-based adaptive learning platform designed for multilingual and low-resource educational contexts, with a case study on Nigerian Pidgin English. The system integrates fine-tuned large language models (LLMs) within a personalised and adaptive learning (PAL) framework, addressing linguistic inclusivity and computational constraints in resource-limited environments. To enhance linguistic alignment, a curated Nigerian Pidgin corpus was developed and used to fine-tune an instruction-tuned LLM. The study further investigates model optimisation through multi-level quantisation (4-bit, 5-bit, and 8-bit), enabling systematic analysis of trade-offs between semantic fidelity and computational efficiency. Experimental evaluation combines automatic semantic metrics (BLEU, ROUGE-L, BERTScore, perplexity, lexical diversity) with human-centred cultural assessment conducted by native speakers. Results demonstrate that higher-bit quantisation improves semantic preservation and structural coherence, while lower-bit models offer reduced inference latency with minimal degradation in instructional quality. The findings establish a deployable, resource-aware intelligent learning system that balances semantic robustness, cultural relevance, and computational efficiency. This work contributes an experimentally validated framework for adapting large language models to low-resource languages while maintaining practical feasibility for scalable educational deployment.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Low-Resource LLM Adaptation
Multi-level Quantization
Nigerian Pidgin Corpus
Cultural Alignment Evaluation
Adaptive Learning Platform
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