From Voices to Validity: Leveraging Large Language Models for Educational Policy Analysis
This paper argues that the integration of Large Language Models (like GPT-4) with human expertise can significantly enhance the analysis of educational policy stakeholder interviews, as demonstrated through a mixed-methods study where GPT-4 achieved 77.89% alignment with human coding for thematic analysis and showed superior performance over traditional NLP methods, while offering new perspectives and maintaining coding consistency for policy research.
Empowering Teachers as AI Architects: The COALESCE Framework for Educational Technology Design
This paper argues that transforming educators from passive users to active co-creators in AI educational tool development, through the COALESCE framework, leads to more effective and contextually relevant technology solutions, as demonstrated through a comprehensive study with K-12 mathematics teachers that showed significant improvements in lesson planning efficiency and technology adoption rates.
Beyond Algorithms: Professional Knowledge in AI-Powered Mathematics Teaching
The paper argues for the essential role of mathematics educators’ professional expertise in human-centered approach to plan high-quality, ambitious mathematics instruction utilizing AI-powered tools.