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
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.