面向多语言与低资源教育场景的基于AI的自适应学习平台：以尼日利亚为例
An AI-Based Adaptive Learning Platform for Multilingual and Low-Resource Educational Contexts: A Case Study on Nigeria
AI总结：
该研究针对低资源多语言教育场景，构建了整合微调LLM的PAL自适应学习平台，经多级量化优化与多维度评估，实现了语义鲁棒性、文化相关性与计算效率的平衡，为低资源语言教育AI系统提供了可部署框架。
AI中文摘要：
在尼日利亚等低资源多语言场景中，教育平台常面临个性化不足、语言支持欠缺、课程国际化薄弱的问题，导致学习者参与度与包容性降低。本文提出一种面向多语言与低资源教育场景的基于AI的自适应学习平台，并以尼日利亚皮钦英语为案例开展研究。该系统在个性化自适应学习（PAL）框架中整合了微调后的大语言模型（LLM），解决资源受限环境中的语言包容性与计算约束问题。为提升语言适配性，研究构建了精心整理的尼日利亚皮钦英语语料库，并用于微调指令调优的大语言模型。此外，研究通过多级量化（4位、5位、8位）开展模型优化，系统分析语义保真度与计算效率间的权衡关系。实验评估结合了自动语义指标（BLEU、ROUGE-L、BERTScore、困惑度、词汇多样性）与母语使用者开展的以人类为中心的文化评估。结果显示，高位量化可提升语义保留度与结构连贯性，而低位模型则能降低推理延迟且教学质量退化极小。研究成果构建了可部署、感知资源的智能学习系统，平衡了语义鲁棒性、文化相关性与计算效率，为将大语言模型适配至低资源语言并保持可扩展教育部署的实践可行性提供了经实验验证的框架。
英文摘要：
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.
