COGENT: A Curriculum-oriented Framework for Generating Grade-appropriate Educational Content
Project Overview
The document explores the use of generative AI in education, focusing on the COGENT framework designed to create grade-appropriate educational content that adheres to curriculum standards. It addresses the challenges of ensuring both curriculum alignment and readability, particularly in STEM subjects, while presenting experimental findings that indicate COGENT's ability to generate content that matches or exceeds the quality of human-written materials. By integrating structured curriculum elements and employing a 'wonder-based' approach to foster student engagement, COGENT has shown notable improvements in both curriculum alignment and comprehensibility. Overall, the findings suggest that generative AI can play a significant role in enhancing educational content delivery and effectiveness.
Key Applications
AI-Generated Educational Content and Assessment
Context: Applicable across various educational contexts, including elementary education (grades 1-5) for young students, personalized learning paths for K-12 students based on assessments, and language learning and assessment.
Implementation: Utilizes large language models (LLMs) to generate educational content, including curriculum-oriented passages, personalized learning paths, and test questions. The implementation focuses on adapting content to meet educational standards and individual student needs, ensuring readability and comprehensibility while maintaining alignment with curriculum frameworks.
Outcomes: ['Generates grade-appropriate educational content and assessments', 'Improves curriculum alignment and comprehensibility', 'Provides customized learning materials based on individual learning status, preferences, and goals', 'Produces high-quality test questions that meet educational standards']
Challenges: ['Maintaining consistent grade-appropriate reading levels across diverse content', 'Aligning with varied curriculum standards while focusing on individualized learning trajectories', 'Establishing comprehensive evaluation standards to ensure the quality and effectiveness of generated assessments']
Implementation Barriers
Technical barrier
Generative AI models often fail to align with established curriculum standards and maintain grade-appropriate readability.
Proposed Solutions: Implement structured curriculum guidance and readability controls in the content generation process.
Pedagogical barrier
The generated content sometimes does not adequately differentiate reading levels or effectively bridge scientific terminology with everyday language.
Proposed Solutions: Adopt a 'wonder-based' approach to engage students and make complex ideas more accessible.
Evaluation barrier
Lack of comprehensive evaluation standards for AI-generated content hinders trust and interest from educators.
Proposed Solutions: Develop broader evaluation criteria that include curriculum alignment, pedagogical scaffolding, and grade-level appropriateness.
Project Team
Zhengyuan Liu
Researcher
Stella Xin Yin
Researcher
Dion Hoe-Lian Goh
Researcher
Nancy F. Chen
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Zhengyuan Liu, Stella Xin Yin, Dion Hoe-Lian Goh, Nancy F. Chen
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