Post-generation control in AI-assisted math visual creation yields higher teacher ratings for predictability and correctness than pre- or mid-generation control, with qualitative trade-offs in agency and effort.
Enhancing Instructional Quality: Leveraging Computer-Assisted Textual Analysis to Generate In-Depth Insights from Educational Artifacts
2 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
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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.
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LLM graders achieve substantial human agreement on math and science MCAS items but vary on ELA, performing best as sources of formative narrative feedback rather than summative numerical scores.
citing papers explorer
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When Should Teachers Control AI Generation for Mathematics Visuals?
Post-generation control in AI-assisted math visual creation yields higher teacher ratings for predictability and correctness than pre- or mid-generation control, with qualitative trade-offs in agency and effort.
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Creating and Evaluating K-12 GenAI Assessment Graders Through Context Engineering
LLM graders achieve substantial human agreement on math and science MCAS items but vary on ELA, performing best as sources of formative narrative feedback rather than summative numerical scores.