Visualizing Intelligent Tutor Interactions for Responsive Pedagogy
Project Overview
The document explores the integration of generative AI in education, particularly through intelligent tutoring systems (ITS) like VisTA, a visual analytics tool designed to enhance teachers' understanding of student interactions with these tutors. VisTA offers visualizations of student problem-solving pathways, engagement metrics, and performance data, which empower educators to customize their instructional approaches based on comprehensive insights. The study underscores the advantages of employing visual analytics to improve responsive pedagogy and tackle the challenges educators encounter when incorporating technology into their teaching practices. Overall, the findings suggest that generative AI can significantly enhance educational outcomes by providing teachers with the necessary tools to analyze and respond to student needs effectively.
Key Applications
Apprentice Tutors and VisTA
Context: University classroom settings for mathematics tutoring
Implementation: VisTA was developed based on teacher feedback and a design study, implemented as part of the Apprentice Tutors platform.
Outcomes: Teachers reported improved understanding of student problem-solving processes, more effective integration of tutors into their curriculum, and tailored responses to individual student needs.
Challenges: Initial difficulty in interpreting complex student interaction data; teachers required support to analyze fine-grained logs effectively.
Implementation Barriers
Technical barrier
Teachers faced challenges analyzing and interpreting complex interaction data from intelligent tutoring systems.
Proposed Solutions: Development of the VisTA visualization tool to help summarize and interpret data effectively.
Pedagogical barrier
Teachers were unsure how to integrate intelligent tutoring systems into their curriculum and course planning.
Proposed Solutions: User evaluation and feedback sessions to adjust the design of visualizations, ensuring they meet teachers' instructional needs.
Project Team
Grace Guo
Researcher
Aishwarya Mudgal Sunil Kumar
Researcher
Adit Gupta
Researcher
Adam Coscia
Researcher
Chris MacLellan
Researcher
Alex Endert
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Grace Guo, Aishwarya Mudgal Sunil Kumar, Adit Gupta, Adam Coscia, Chris MacLellan, Alex Endert
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai