Choose Your Own Question: Encouraging Self-Personalization in Learning Path Construction
Project Overview
The document explores the innovative application of generative AI in education through the development of Rocket, a user-friendly interface designed to enhance Interactive Educational Systems (IESs). Rocket addresses existing limitations in IESs, such as the lack of student feedback mechanisms and transparency in content recommendations. By facilitating self-personalization, Rocket empowers students to curate their learning experiences, allowing them to select materials that align with their preferences and learning styles. The system leverages AI to extract insights and visually represent students' learning progress, thereby promoting engagement and explainability. The findings suggest that such advancements not only improve student satisfaction and involvement but also foster a more tailored educational journey, ultimately enhancing learning outcomes.
Key Applications
Rocket - a Tinder-like User Interface for learning material selection
Context: Interactive Educational Systems (IES) targeting students seeking personalized learning paths.
Implementation: Implemented as a mobile touchscreen or web application allowing students to swipe or tap to choose learning materials.
Outcomes: Improved student engagement, enhanced explainability of recommendations, and better tracking of learning progress.
Challenges: Ensuring the system accommodates diverse learning preferences and maintaining student interest over time.
Implementation Barriers
Technical Barrier
Limited innovation in User Interfaces of existing IESs, leading to a lack of engagement and transparency. Additionally, students have limited insight into the recommendation process, which can reduce trust in the IES.
Proposed Solutions: Development of Rocket that includes visual summaries and allows for self-personalization based on student choices. Rocket enhances transparency by showing AI-extracted features used in recommendations.
Project Team
Youngduck Choi
Researcher
Yoonho Na
Researcher
Youngjik Yoon
Researcher
Jonghun Shin
Researcher
Chan Bae
Researcher
Hongseok Suh
Researcher
Byungsoo Kim
Researcher
Jaewe Heo
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Youngduck Choi, Yoonho Na, Youngjik Yoon, Jonghun Shin, Chan Bae, Hongseok Suh, Byungsoo Kim, Jaewe Heo
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