AUTOMATIC MULTIPLE-CHOICE QUESTION GENERATION USING ARTIFICIAL INTELLIGENCE

Abstract

This paper presents a local pipeline for generating multiple-choice questions for IGCSE and A-Level computer science. From 3,385 source questions, duplicate removal and answer-position balancing produced 5,702 training and validation examples. Qwen2.5-7B-Instruct was adapted using QLoRA on an NVIDIA RTX 3060 with 12 GB of memory, and generated candidates underwent automatic structural validation. An author audit judged 15 of 18 candidates usable without substantive correction. A subsequent external expert review rated 51 of 57 items (89.5%) usable as generated and six usable after minor revision; no factual errors were recorded. Answer leakage and repeated stems remained important defects. The study documents the data-preparation procedure, training configuration, and evaluation limitations. The findings support a teacher-in-the-loop proof of concept, while small samples, limited rater independence, missing controlled baselines, and unresolved source-data permissions constrain broader claims.

Keywords: automatic question generation, multiple-choice questions, large language models, QLoRA, computer science education.

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How to cite

Amirlan Seitkadyrov, Akezhan Omashev AUTOMATIC MULTIPLE-CHOICE QUESTION GENERATION USING ARTIFICIAL INTELLIGENCE // Central Asian Scientific Journal. – 2026. – No. 4(32), Vol. 1. – Pp. 287–295.