EDBooks: AI-Enhanced Interactive Narratives for Programming Education
Project Overview
The document explores the innovative use of generative AI in education through the development and evaluation of EDBook, a platform that combines Large Language Models (LLMs) with conventional learning resources to enhance programming education. EDBook aims to foster personalized learning experiences by facilitating dialogic interactions, enabling students to ask questions and receive customized responses while adhering to a structured learning trajectory. The findings emphasize EDBook's effectiveness in boosting student engagement and promoting active learning, as well as its capability to assess understanding through interactive content. Overall, the integration of generative AI via EDBook demonstrates significant potential for transforming educational practices by tailoring learning experiences to individual needs, thereby improving outcomes in programming education.
Key Applications
EDBook: AI-Enhanced Interactive Narratives for Programming Education
Context: Asynchronous programming education for learners, particularly in programming courses.
Implementation: EDBook combines goal-oriented dialog trees with open-ended LLM interactions to provide structured yet flexible learning experiences.
Outcomes: Increased engagement, personalized learning experiences, and improved assessment scores. Participants spent more time learning and attempted more coding quizzes.
Challenges: Balancing open-ended exploration with structured learning goals; ensuring LLM responses are accurate and pedagogically sound.
Implementation Barriers
Technical Barrier
Difficulty in ensuring that LLM responses align with educational goals and are accurate.
Proposed Solutions: Implementing mechanisms to constrain LLM outputs and using dialog trees to guide learning.
Engagement Barrier
Potential lack of critical thinking due to reliance on pre-written responses.
Proposed Solutions: Encouraging more user-driven exploration and interaction with the content.
Navigational Barrier
Navigating content can be challenging due to the dialog format and less refined search functionalities compared to standard web interfaces.
Proposed Solutions: Improving navigability through more concise content and enhanced search features.
Project Team
Steve Oney
Researcher
Yue Shen
Researcher
Fei Wu
Researcher
Young Suh Hong
Researcher
Ziang Wang
Researcher
Yamini Khajekar
Researcher
Jiacheng Zhang
Researcher
April Yi Wang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Steve Oney, Yue Shen, Fei Wu, Young Suh Hong, Ziang Wang, Yamini Khajekar, Jiacheng Zhang, April Yi Wang
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