ConQuer: A Framework for Concept-Based Quiz Generation
Project Overview
The document explores the application of generative AI in education through the introduction of ConQuer, a concept-based quiz generation framework that utilizes external knowledge sources to enhance quiz quality produced by large language models (LLMs). It acknowledges the challenges associated with quiz creation, particularly regarding quality and relevance, while demonstrating the potential of LLMs to improve efficiency. ConQuer achieved a 4.8% increase in evaluation scores and a 77.52% success rate in pairwise comparisons with baseline quiz sets, underscoring its effectiveness. By focusing on the extraction of key concepts from student inquiries, the framework aims to generate quizzes that are comprehensive and relevant, ultimately enhancing student learning outcomes across various educational levels. This innovative use of generative AI highlights its promising role in supporting educational practices and improving assessment methods.
Key Applications
ConQuer, a concept-based quiz generation framework
Context: Educational settings across primary, high school, and PhD levels, targeting students needing quizzes to reinforce their understanding.
Implementation: The framework utilizes LLMs to extract key concepts from student questions, retrieves relevant information from external knowledge sources, and generates personalized quizzes based on this content.
Outcomes: Achieved a 4.8% improvement in quiz quality evaluation scores and a 77.52% win rate in pairwise comparisons against traditional LLM-generated quizzes.
Challenges: Concerns about quiz quality and accuracy, potential for generation of irrelevant or incomplete quizzes.
Implementation Barriers
Quality of AI Outputs
LLMs may generate quizzes that lack accuracy or relevance to the key concepts required for effective learning.
Proposed Solutions: Using concept extraction and external knowledge sources to ensure quizzes are grounded in reliable information.
Latency in Generation
The inherent latency of LLMs can hinder the rapid generation of quizzes that students expect in interactive learning environments.
Proposed Solutions: Integrating adaptive computing strategies and supplementary LLM serving systems.
Ambiguity in Student Questions
Critical concepts may be overlooked or misinterpreted, especially when student questions are vague or contain implicit ideas.
Proposed Solutions: Enhancing concept extraction techniques to better capture underlying concepts.
Project Team
Yicheng Fu
Researcher
Zikui Wang
Researcher
Liuxin Yang
Researcher
Meiqing Huo
Researcher
Zhongdongming Dai
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Yicheng Fu, Zikui Wang, Liuxin Yang, Meiqing Huo, Zhongdongming Dai
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