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Apprentice Tutor Builder: A Platform For Users to Create and Personalize Intelligent Tutors

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arxiv 2404.07883 v1 pith:FM45AOYE submitted 2024-04-11 cs.HC cs.AI

classification cs.HCcs.AI
keywords tutorbuilderdesigninstructorsplatformusersagentapprentice
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Can LLMs Reliably Simulate Real Students' Abilities in Mathematics and Reading Comprehension?

    cs.CL 2025-07 conditional novelty 5.0 of 10

    No LLM-prompt pair among 11 models and 4 prompts aligns with average NAEP student performance across math and reading in grades 4, 8, and 12.

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