In the waning days of the COVID epidemic the education community and, more widely the press, began to use the team learning loss to explain the drop in student performance. Learning loss and poor student performance in general invariably pointed to the student as the point of failure. As researchers we asked the question what if it wasn’t the students but perhaps a teaching, curriculum or assessment failure. With that in mind we set out to identify, in operational educational environments if AI could facilitate a gap analysis from the beginning to the end of the lifecycle of that knowledge. Using past research and live data from US Medical Schools, often thought to be among the most rigorous educational environments, we found a need for AI tools to help the education community align standards, curriculum, instruction, and assessment.
Curriculum alignment research demonstrates positive relationships between paired alignment categories—standards, written curriculum, taught curriculum, and tested curriculum—yet systematic degradation occurs at stakeholder handoffs despite professional competence and good intentions (Porter, 2002; Webb, 2007). This presentation explores how artificial intelligence platforms can identify and remediate alignment gaps across the complete curriculum development chain.
Traditional alignment research focuses on dyadic relationships while overlooking cumulative degradation effects. Our multi-district analysis reveals that while standards-to-written curriculum alignment averages 0.78, end-to-end coherence drops to 0.41, representing 15-20% decay per stakeholder transition (Schmidt et al., 2001). Key degradation mechanisms include interpretive drift, priority shifts reflecting stakeholder expertise differences, and constraint-driven implementation compromises.
AI-powered curriculum analysis addresses these challenges through automated gap identification and real-time alignment monitoring. AI platforms utilize natural language processing to analyze curriculum documents, lesson plans, assessment items, and instructional lectures and materials, generating quantitative alignment matrices across all four curriculum categories simultaneously. Machine learning algorithms identify semantic disconnects, content gaps, and cognitive demand mismatches that human reviewers often miss due to organizational silos and time constraints.
Implementation case studies demonstrate AI's capacity to: (1) conduct comprehensive cross-document analysis identifying specific misalignment points; (2) generate actionable recommendations for stakeholder teams; (3) provide continuous monitoring as curricula evolve; and (4) create feedback loops ensuring maintained coherence across development phases. We will explore data from multiple US Medical Schools.
These findings suggest that AI technology can serve as a crucial bridge between stakeholder groups, maintaining alignment fidelity while preserving necessary expertise specialization. The presentation will demonstrate practical implementation strategies and discuss implications for curriculum development workflows in the age of educational AI.
View the full presentation here:
Edelblut, P. (2025, September 29). From Standards to Assessment: Using AI to Create Efficacy in the Educational Experience. AIEOU Inaugural Conference, University of Oxford. Zenodo. https://doi.org/10.5281/zenodo.17227656
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