Towards Educator-Driven Tutor Authoring: Generative AI Approaches for Creating Intelligent Tutor Interfaces
Project Overview
The document explores the significant role of generative AI in education, particularly through its application in Intelligent Tutoring Systems (ITSs). It highlights the innovative use of Large Language Models (LLMs) and prompt engineering to develop an AI-enhanced Tutor Builder, which enables educators to create intuitive tutor interfaces without the need for advanced programming skills. This tool facilitates the generation of complete tutor layouts or specific components, thereby streamlining the design process and making educational technology more accessible to non-technical educators. The findings suggest that such integration not only enhances usability but also encourages the adoption of adaptive education technologies, ultimately aiming to improve learning outcomes and foster a more personalized educational experience. By empowering educators with these AI tools, the document reveals a transformative potential in the landscape of educational resources and instructional design.
Key Applications
AI-enhanced Tutor Builder
Context: Used by educators to create Intelligent Tutoring Systems (ITSs) that provide personalized learning experiences.
Implementation: Generative AI capabilities are integrated into the existing Apprentice Tutor Builder (ATB), allowing educators to generate layouts and components based on their requirements.
Outcomes: Increased efficiency in designing tutor interfaces, with reported time reductions of up to 68% for complex designs.
Challenges: The need for further validation of AI-generated interfaces' quality and effectiveness; potential over-reliance on AI by educators.
Implementation Barriers
Technical Barrier
The need for specialized programming and design skills has historically limited educators' ability to develop effective ITSs.
Proposed Solutions: Introduce no-code authoring tools and generative AI enhancements to simplify the design process for non-technical educators.
Project Team
Tommaso Calo
Researcher
Christopher J. MacLellan
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Tommaso Calo, Christopher J. MacLellan
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