# Automated Generation of Curriculum-Aligned Multiple-Choice Questions for Malaysian Secondary Mathematics Using Generative AI

arXiv: https://arxiv.org/abs/2508.04442 · HTML: https://mlsanity.com/paper/2508.04442/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Savaal: Scalable Concept-Driven Question Generation to Enhance Human Learning](https://mlsanity.com/paper/2502.12477/) (2025)
- [Generative AI and Large Language Models in Language Preservation: Opportunities and Challenges](https://mlsanity.com/paper/2501.11496/) (2025)
- [Application of Large Language Models in Automated Question Generation: A Case Study on ChatGLM's Structured Questions for National Teacher Certification Exams](https://mlsanity.com/paper/2408.09982/) (2024)
- [Leveraging Large Language Models to Generate Course-specific Semantically Annotated Learning Objects](https://mlsanity.com/paper/2412.04185/) (2024)
- [Automatic Teaching Platform on Vision Language Retrieval Augmented Generation](https://mlsanity.com/paper/2503.05464/) (2025)
- [Machine Assistant with Reliable Knowledge: Enhancing Student Learning via RAG-based Retrieval](https://mlsanity.com/paper/2506.23026/) (2025)

## Built on by (most cited first, 2 of 2)

- [Judging the Judges: Human Validation of Multi-LLM Evaluation for High-Quality K--12 Science Instructional Materials](https://mlsanity.com/paper/2602.13243/) (2026)
- [SciEval: A Benchmark for Automatic Evaluation of K-12 Science Instructional Materials](https://mlsanity.com/paper/2604.25472/) (2026)

## Abstract (arXiv)

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.