Automatic Question Generation with Large Language Models: A Survey

Abstract

Test-based learning is effective in fostering knowledge retention, but manually creating assessment questions remains time-consuming and limits personalized student practice. The advent of Large Language Models (LLMs) has introduced new possibilities for Automatic Question Generation (AQG). Motivated by this context, this survey provides a comprehensive overview of AQG using LLMs, focusing on educational applications. Following the PRISMA methodology, we reviewed 132 studies published between 2023 and 2025. Our contributions include a taxonomy of question types by response openness, an analysis of AQG efforts across knowledge fields, educational levels, evaluation strategies, and difficulty control. We also identify recurring challenges and research opportunities.