Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models

TL;DR

This paper introduces a novel method combining knowledge graphs and large language models to generate multiple-choice questions with interpretable difficulty estimation, enhancing automated educational content creation.

Contribution

It presents a new approach that constructs knowledge graphs from documents and uses them with LLMs to generate MCQs with aligned difficulty scores.

Findings

Generated MCQs are of high quality and align with human difficulty perceptions.

The difficulty estimation method is interpretable and data-driven.

The approach improves automated MCQ generation by integrating structured knowledge and LLMs.

Abstract

Generating multiple-choice questions (MCQs) with difficulty estimation remains challenging in automated MCQ-generation systems used in adaptive, AI-assisted education. This study proposes a novel methodology for generating MCQs with difficulty estimation from the input documents by utilizing knowledge graphs (KGs) and large language models (LLMs). Our approach uses an LLM to construct a KG from input documents, from which MCQs are then systematically generated. Each MCQ is generated by selecting a node from the KG as the key, sampling a related triple or quintuple -- optionally augmented with an extra triple -- and prompting an LLM to generate a corresponding stem from these graph components. Distractors are then selected from the KG. For each MCQ, nine difficulty signals are computed and combined into a unified difficulty score using a data-driven approach. Experimental results demonstrate that our method generates high-quality MCQs whose difficulty estimation is interpretable and aligns with human perceptions. Our approach improves automated MCQ generation by integrating structured knowledge representations with LLMs and a data-driven difficulty estimation model.