Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts
Zewei Tian, Min Sun, Alex Liu, Shawon Sarkar, Jing Liu
TL;DR
The paper surveys how AI/ML, especially NLP and generative AI, can extract in-depth, actionable insights from educational artifacts to improve instructional quality, using Elmore's Instructional Core as the organizing lens. It presents teacher-facing techniques (dialogic NLP analysis of classroom discourse and AI-assisted coaching), student-facing applications (auto-grading, ITS), and content-focused analyses (OER evaluation and content generation), supported by multiple case studies. Key findings show potential for scalable feedback, personalized learning, and resource development, but also emphasize data quality, ethical considerations, and the need for human expertise and fine-tuning. The work highlights four future directions: integrative, domain-informed AI systems; multi-modal generative models; human-centered learning and AI literacy; and robust computational infrastructure to sustain large-scale educational analytics and content creation.
Abstract
This paper explores the transformative potential of computer-assisted textual analysis in enhancing instructional quality through in-depth insights from educational artifacts. We integrate Richard Elmore's Instructional Core Framework to examine how artificial intelligence (AI) and machine learning (ML) methods, particularly natural language processing (NLP), can analyze educational content, teacher discourse, and student responses to foster instructional improvement. Through a comprehensive review and case studies within the Instructional Core Framework, we identify key areas where AI/ML integration offers significant advantages, including teacher coaching, student support, and content development. We unveil patterns that indicate AI/ML not only streamlines administrative tasks but also introduces novel pathways for personalized learning, providing actionable feedback for educators and contributing to a richer understanding of instructional dynamics. This paper emphasizes the importance of aligning AI/ML technologies with pedagogical goals to realize their full potential in educational settings, advocating for a balanced approach that considers ethical considerations, data quality, and the integration of human expertise.