Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors
This work addresses the problem of making intelligent tutoring systems more accessible and customizable for instructors, though it appears incremental as it builds on existing interactive AI agent methods for tutor creation.
The paper tackles the complexity and specialized knowledge required to develop intelligent tutoring systems by introducing the Apprentice Tutor Builder platform, which simplifies tutor creation and personalization through a drag-and-drop interface and interactive AI agent training, as evaluated in a user study with 14 instructors who found it flexible and easy to use but desired more time-saving features.
Intelligent tutoring systems (ITS) are effective for improving students' learning outcomes. However, their development is often complex, time-consuming, and requires specialized programming and tutor design knowledge, thus hindering their widespread application and personalization. We present the Apprentice Tutor Builder (ATB) , a platform that simplifies tutor creation and personalization. Instructors can utilize ATB's drag-and-drop tool to build tutor interfaces. Instructors can then interactively train the tutors' underlying AI agent to produce expert models that can solve problems. Training is achieved via using multiple interaction modalities including demonstrations, feedback, and user labels. We conducted a user study with 14 instructors to evaluate the effectiveness of ATB's design with end users. We found that users enjoyed the flexibility of the interface builder and ease and speed of agent teaching, but often desired additional time-saving features. With these insights, we identified a set of design recommendations for our platform and others that utilize interactive AI agents for tutor creation and customization.