使用生成式AI自动生成与马来西亚中学数学课程对齐的多项选择题
Automated Generation of Curriculum-Aligned Multiple-Choice Questions for Malaysian Secondary Mathematics Using Generative AI
AI总结：
本文提出并比较四种基于GPT-4o的流水线，用于生成马来语初一数学选择题，采用RAG方法显著提升课程对齐与事实准确性，并引入RAG-QA评估技术。
摘要：
本文针对马来西亚教育系统中对可扩展且高质量教育评估工具的迫切需求。文章强调了生成式AI（GenAI）的潜力，同时承认在确保事实准确性和课程对齐方面面临的重大挑战，尤其是对于像马来语这样的低资源语言。本研究介绍并比较了四种递增式流水线，使用OpenAI的GPT-4o生成马来语的初一数学多项选择题（MCQs）。这些方法从非接地提示（结构化和基础）到检索增强生成（RAG）方法（一种使用LangChain框架，一种手动实现）。该系统以官方课程文件为基础，包括教师准备的笔记和年度教学计划（RPT）。采用双管齐下的自动化评估框架来评估生成的问题。课程对齐通过语义文本相似度（STS）与RPT进行衡量，而上下文有效性则通过一种新颖的基于RAG的问答（RAG-QA）方法进行验证。结果表明，基于RAG的流水线显著优于非接地提示方法，生成的问题具有更高的课程对齐度和事实有效性。研究进一步分析了基于框架的RAG易于实现与手动流水线提供细粒度控制之间的权衡。这项工作提出了一种在低资源语言中生成课程特定教育内容的验证方法，引入了一种共生RAG-QA评估技术，并为在马来西亚及类似地区开发和部署实用教育技术解决方案提供了可操作的见解。
英文摘要：
This paper addresses the critical need for scalable and high-quality educational assessment tools within the Malaysian education system. It highlights the potential of Generative AI (GenAI) while acknowledging the significant challenges of ensuring factual accuracy and curriculum alignment, especially for low-resource languages like Bahasa Melayu. This research introduces and compares four incremental pipelines for generating Form 1 Mathematics multiple-choice questions (MCQs) in Bahasa Melayu using OpenAI's GPT-4o. The methods range from non-grounded prompting (structured and basic) to Retrieval-Augmented Generation (RAG) approaches (one using the LangChain framework, one implemented manually). The system is grounded in official curriculum documents, including teacher-prepared notes and the yearly teaching plan (RPT). A dual-pronged automated evaluation framework is employed to assess the generated questions. Curriculum alignment is measured using Semantic Textual Similarity (STS) against the RPT, while contextual validity is verified through a novel RAG-based Question-Answering (RAG-QA) method. The results demonstrate that RAG-based pipelines significantly outperform non-grounded prompting methods, producing questions with higher curriculum alignment and factual validity. The study further analyzes the trade-offs between the ease of implementation of framework-based RAG and the fine-grained control offered by a manual pipeline. This work presents a validated methodology for generating curriculum-specific educational content in a low-resource language, introduces a symbiotic RAG-QA evaluation technique, and provides actionable insights for the development and deployment of practical EdTech solutions in Malaysia and similar regions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
