MCQs Generation With Large Language Models: A Survey of Methodologies, Evolution, and Open Research Issues

In recent years, there has been growing interest in the automatic generation of Multiple Choice Questions (MCQs), mainly to reduce the effort and time involved in manual question construction. Many researchers and practitioners have tried different methods to produce accurate and good-quality MCQs automatically. The progress in this area has been steady but also diverse, which makes it difficult to identify which approaches work best in practice. The research trend has moved gradually from early rule-based methods to more advanced systems based on Large Language Models (LLMs). Although several review papers and surveys have summarized earlier work, most of them give limited discussion on how the field has evolved over time or what key problems remain to be solved. This paper aims to fill that gap by reviewing important studies on MCQs generation and providing a structured overview of the main question types, their essential components, and the processes involved in automated generation. It also discusses the essential stages in a general system architecture, including preprocessing, key selection, and distractor generation. In addition, the review summarizes the most frequently used datasets and evaluation measures for assessing question quality and highlights important applications and research challenges. In doing so, it outlines possible future directions to advance the use of automated MCQs generation in education, training, and professional assessment.