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.

翻译：基于问题的活动（QBAs）在教育中广泛应用，传统上构成学习与评估过程的重要组成部分。本文设计并评估了一种用于学校形成性评估和总结性评估的自动题目生成工具。通过对一百零四位教师的专家调查，我们证明了自动生成QBAs的必要性——该工具能显著减轻教师工作量并促进个性化学习体验。借助生成式人工智能的最新进展，我们提出了一种模块化框架，采用基于Transformer的语言模型从文本内容自动生成选择题（MCQs）。该方案包含题目生成、正确答案预测和干扰项构建三个独立模块，可用于评估不同语言模型与生成技术。最后，我们进行了广泛的定量与定性评估，揭示了不同技术与模型使用中的权衡关系。