Intelligent Tutoring Systems (ITSs) have shown great potential in delivering personalized and adaptive education, but their widespread adoption has been hindered by the need for specialized programming and design skills. Existing approaches overcome the programming limitations with no-code authoring through drag and drop, however they assume that educators possess the necessary skills to design effective and engaging tutor interfaces. To address this assumption we introduce generative AI capabilities to assist educators in creating tutor interfaces that meet their needs while adhering to design principles. Our approach leverages Large Language Models (LLMs) and prompt engineering to generate tutor layout and contents based on high-level requirements provided by educators as inputs. However, to allow them to actively participate in the design process, rather than relying entirely on AI-generated solutions, we allow generation both at the entire interface level and at the individual component level. The former provides educators with a complete interface that can be refined using direct manipulation, while the latter offers the ability to create specific elements to be added to the tutor interface. A small-scale comparison shows the potential of our approach to enhance the efficiency of tutor interface design. Moving forward, we raise critical questions for assisting educators with generative AI capabilities to create personalized, effective, and engaging tutors, ultimately enhancing their adoption.

翻译：智能导师系统（ITS）在提供个性化与适应性教育方面展现出巨大潜力，但其广泛应用一直受限于对专业编程与设计技能的需求。现有方法通过拖放式无代码创作克服了编程限制，但这类方法默认教育者具备设计高效且具吸引力导师界面的必要技能。为应对这一假设，我们引入生成式人工智能能力，协助教育者创建既符合其需求又遵循设计原则的导师界面。我们的方法利用大语言模型（LLMs）与提示工程技术，基于教育者提供的高层次需求输入生成导师界面布局与内容。然而，为使教育者能主动参与设计过程而非完全依赖AI生成方案，我们支持完整界面层级与独立组件层级的双重生成模式：前者为教育者提供可通过直接操作优化的完整界面，后者则支持创建待添加至导师界面的特定元素。小规模对比实验验证了本方法在提升导师界面设计效率方面的潜力。展望未来，我们提出若干关键问题，探讨如何借助生成式人工智能能力协助教育者创建个性化、高效且具吸引力的导师系统，最终推动其普及应用。