GPTutor: Great Personalized Tutor with Large Language Models for Personalized Learning Content Generation
Project Overview
The document discusses GPTutor, a generative AI-driven web application that personalizes educational experiences by utilizing large language models (LLMs) to tailor content and practice exercises to individual students' interests and career aspirations. This innovative approach seeks to improve student engagement and comprehension while making knowledge more applicable to their future careers. Built on a serverless architecture, GPTutor offers scalability and user-friendliness for both teachers and students, facilitating the easy creation of customized learning materials. Educators can upload their resources, which the AI then modifies to fit each learner's unique needs. Overall, the integration of generative AI in education through GPTutor exemplifies a significant advancement in personalized learning, aiming to enhance educational outcomes by making learning more relevant and engaging for students.
Key Applications
GPTutor - a web application for personalized learning content generation
Context: Educational context for high school and university students seeking personalized learning experiences.
Implementation: Teachers upload course content into GPTutor, where students input their interests and career goals. The system uses OpenAI's GPT-4 API to generate customized educational content.
Outcomes: Enhanced student engagement and understanding of academic concepts; personalized learning journeys that align with career aspirations.
Challenges: Challenges include ensuring the accuracy of generated content and managing the complexity of integrating personalized elements into existing curricula.
Implementation Barriers
Technical Barrier
The complexity of setting up AI-driven personalized learning systems and ensuring the correct input of learning content.
Proposed Solutions: Developing user-friendly interfaces for teachers to upload and edit course materials and employing robust AI models to minimize manual intervention.
Project Team
Eason Chen
Researcher
Jia-En Lee
Researcher
Jionghao Lin
Researcher
Kenneth Koedinger
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Eason Chen, Jia-En Lee, Jionghao Lin, Kenneth Koedinger
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