From First Draft to Final Insight: A Multi-Agent Approach for Feedback Generation
Project Overview
The document explores the application of generative AI, specifically Large Language Models (LLMs), in the educational sector, focusing on their ability to provide high-quality feedback to students. It presents a novel multi-agent approach termed 'G-E-RG' (generation, evaluation, and regeneration), designed to enhance the effectiveness of feedback mechanisms. The findings indicate that implementing the G-E-RG process leads to significant improvements in multiple aspects of feedback quality, thereby helping educators better support student learning. However, the study also acknowledges existing challenges in fully optimizing feedback delivery. Overall, the research highlights the potential of generative AI tools in creating more effective educational experiences, while also pointing out the need for ongoing enhancements to maximize their impact.
Key Applications
Multi-agent approach for feedback generation (G-E-RG)
Context: Graduate course on E-Learning Design Principles and Methods at Carnegie Mellon University.
Implementation: Utilized a GPT-4o model to generate feedback using various prompt techniques and frameworks, followed by evaluation and regeneration phases.
Outcomes: Improved evaluation accuracy, enhanced quality of feedback, and increased engagement in learner-centered interactions.
Challenges: Feedback quality still lacks optimal features such as fostering learner independence and enhancing teacher-student relationships.
Implementation Barriers
Technical Limitations
LLMs can produce inaccurate or verbose feedback, which reduces clarity and effectiveness. Feedback generated still has room for improvement in critical pedagogical elements.
Proposed Solutions: Implement prompt engineering techniques and integrate robust educational theories to improve feedback quality. Incorporate human feedback to refine automated processes and enhance evaluation accuracy.
Resource Intensity
The multi-agent approach is resource-intensive, potentially increasing costs.
Proposed Solutions: Explore methods to streamline the G-E-RG process and reduce resource demands.
Project Team
Jie Cao
Researcher
Chloe Qianhui Zhao
Researcher
Xian Chen
Researcher
Shuman Wang
Researcher
Christian Schunn
Researcher
Kenneth R. Koedinger
Researcher
Jionghao Lin
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Jie Cao, Chloe Qianhui Zhao, Xian Chen, Shuman Wang, Christian Schunn, Kenneth R. Koedinger, Jionghao Lin
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