Apprentice Tutor Builder: A Platform for Teachers to Create and Personalize Intelligent Tutors

核心概念

The Apprentice Tutor Builder (ATB) is a platform that simplifies the creation and personalization of intelligent tutoring systems, empowering teachers to build tutor interfaces and author expert models without specialized programming knowledge.

摘要

The Apprentice Tutor Builder (ATB) is a platform designed to simplify the creation and customization of intelligent tutoring systems (ITSs). The key components of ATB are:

Tutor Builder:

• Allows users to design tutor interfaces using a drag-and-drop, row-column layout approach.
• Provides flexibility for users to create diverse and stylistically varied interfaces.

Apprentice Agent Training:

• Enables users to interactively train the underlying AI agent of the tutor.
• Users can provide demonstrations, feedback, and labels to teach the agent how to solve problems.
• The agent learns via a Hierarchical Task Network (HTN) representation and updates its knowledge based on user interactions.

The authors conducted a user study with 14 instructors to evaluate the usability of ATB. Key findings:

• The row-column layout approach to the interface builder was usable by teachers without specialized training.
• The interactive approach to authoring expert models via demonstrations, feedback, and labels was effective.
• Teachers saw value in the potential of ATB and expressed willingness to engage in tutor creation and customization.
• Participants desired additional features for more flexible interface design and better guidance on when the agent has sufficiently learned a task.

The study demonstrates the potential of interactive AI-based authoring tools to empower teachers to create and personalize intelligent tutors for their classrooms.

統計資料

None

引述

"It was pretty easy...the agent picked up right away after like three problems."
"I find it very useful, especially [working] in the school, and with the students, it's gonna be very, very helpful to teach them."
"It's essentially like coaching one student."

從以下內容提煉的關鍵洞見

Apprentice Tutor Builder

by Glen Smith,A... 於 arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07883.pdf

深入探究

How can ATB be extended to support collaborative authoring of tutors among teachers?

To enable collaborative authoring of tutors among teachers in ATB, several features can be implemented. Firstly, a version control system can be integrated to track changes made by different users, allowing for easy collaboration and version history. Additionally, a commenting and feedback system can be included to facilitate communication between collaborators, enabling them to provide input and suggestions on each other's work. Furthermore, real-time collaborative editing features can be implemented, allowing multiple users to work on the same tutor simultaneously. This would enhance teamwork and efficiency in creating and customizing tutors.

What are the potential challenges and ethical considerations in allowing students to author their own tutors using ATB?

Allowing students to author their own tutors using ATB presents several challenges and ethical considerations. One challenge is ensuring the accuracy and educational value of the content created by students. There is a risk of students creating misleading or incorrect content, which could impact the learning outcomes of their peers. Ethical considerations include ensuring that students have the necessary knowledge and skills to create effective tutors, as well as addressing issues of academic integrity and plagiarism. Additionally, there may be concerns about data privacy and security if student-created content contains personal information or sensitive data.

How can the agent training process in ATB be further improved to provide clearer guidance on model sufficiency and correctness?

To enhance the agent training process in ATB and provide clearer guidance on model sufficiency and correctness, several improvements can be made. Firstly, implementing a progress tracking system that visually displays the agent's learning progress and performance metrics can help users gauge the sufficiency of the model. Additionally, incorporating automated evaluation tools that assess the model's accuracy and provide feedback to users can enhance the training process. Introducing interactive tutorials or guided training sessions that walk users through best practices for training the agent can also improve clarity and effectiveness. Lastly, integrating a validation mechanism that tests the model on a set of predefined problems and provides instant feedback on its performance can help users determine when the model is sufficiently trained.