  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.
