Multiple-choice questions (MCQs) are widely used across diverse educational fields and levels. Well-designed MCQs should evaluate knowledge application in real-world situations. However, writing such test items in sufficient numbers is challenging and time-consuming, especially in natural science education. The problem of a sufficient number of MCQs has two aspects: content coverage and exam security. Therefore, generating test items involves two tasks: creating MCQ prototypes and transforming these prototypes into item series. In automated item generation, prototype creation aligns with template-based methods like cognitive modelling, while item expansion corresponds to example-based techniques. The aim of this research was designing the goal-oriented conceptual model of human - AI co-creation of MCQs that should meet strictly formulated quality criteria. The resulting three-step model for creating MCQ prototypes distributed prompts between several AIs, with human revision of responses for each prompt before setting the next one. To transform the MCQ prototype into an MCQ series, a one-step model was developed in which multiple new items are generated simultaneously. These items assess the same learning outcome but are not simple rephrasings of the prototype or of one another. Based on human and automated evaluation, approximately half of the output MCQs were acceptable without editing. Minor corrections of initially rejected test items allowed for a moderate increase in acceptance of MCQs in series and a significant improvement of MCQ-prototypes.

翻译：多选题（MCQs）广泛应用于不同教育领域和层级。设计良好的多选题应能评估知识在真实情境中的应用能力。然而，撰写足够数量的此类测试题目具有挑战性且耗时，尤其在自然科学教育中。多选题数量充足的问题涉及两方面：内容覆盖度与考试安全性。因此，生成测试题目包含两项任务：创建多选题原型，并将这些原型转化为题目系列。在自动化题目生成中，原型创建对应于基于模板的方法（如认知建模），而题目扩展则对应基于实例的技术。本研究旨在设计一种目标导向的人机协同创作多选题的概念模型，该模型需满足严格制定的质量标准。最终提出的三步式多选题原型创建模型，将提示词分配至多个AI系统，并在设置下一提示前对每个提示的响应进行人工修订。为将多选题原型转化为题目系列，本研究开发了一步式模型，可同时生成多个新题目。这些题目评估相同的学习目标，且并非对原型或彼此间的简单复述。基于人工与自动化评估，约半数输出的多选题无需修改即可接受。对初始被拒测试题目进行细微修正后，题目系列的接受率得到适度提升，且多选题原型的质量获得显著改善。