Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors
Glen Smith, Adit Gupta, Christopher MacLellan

TL;DR
The paper introduces the Apprentice Tutor Builder, a user-friendly platform enabling instructors to easily create and personalize intelligent tutors through interactive training and a drag-and-drop interface, enhancing accessibility and customization.
Contribution
It presents a novel platform that simplifies intelligent tutor development by combining interactive AI training with an intuitive interface, reducing technical barriers.
Findings
Users appreciated the interface flexibility and ease of training.
Participants found the platform quick and effective for tutor creation.
Design recommendations were derived for future interactive AI tutor systems.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsIntelligent Tutoring Systems and Adaptive Learning
MethodsSparse Evolutionary Training · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
