Automating question generation from educational text

TL;DR

This paper introduces an AI-powered framework for automatically generating multiple-choice questions from educational texts, aiming to reduce teacher workload and enhance personalized learning.

Contribution

It presents a modular transformer-based system for question generation, including evaluation of different models and techniques, addressing a key need in educational assessment.

Findings

The framework effectively generates questions with high quality.

Different models and techniques offer trade-offs in accuracy and diversity.

Teachers see value in automated question generation for workload reduction.

Abstract

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and evaluate an automated question generation tool for formative and summative assessment in schools. We present an expert survey of one hundred and four teachers, demonstrating the need for automated generation of QBAs, as a tool that can significantly reduce the workload of teachers and facilitate personalized learning experiences. Leveraging the recent advancements in generative AI, we then present a modular framework employing transformer based language models for automatic generation of multiple-choice questions (MCQs) from textual content. The presented solution, with distinct modules for question generation, correct answer prediction, and distractor formulation, enables us to evaluate different language models and generation techniques. Finally, we perform an extensive quantitative and qualitative evaluation, demonstrating trade-offs in the use of different techniques and models.