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Generative AI-Assisted Curriculum Auditing: Mapping a High School Mathematics Pathway to O*NET Data Scientist Competencies

October 2026 Qingyun Pei, Haoxin Zhu, Qilong Yu, Yuexi Ding Chinese Institute of Engineers - USA, Greater New York Chapter (CIE-USA GNYC) 2026 Annual Convention
#Generative AI #Curriculum Alignment #Mathematics Education #Educational Data Mining

Abstract

As artificial intelligence and data-intensive technologies continue to reshape scientific and professional practice, secondary mathematics curricula face growing expectations to prepare students for new forms of quantitative reasoning. This poster presents a generative-AI-assisted curriculum audit framework for examining the alignment between a high school mathematics pathway and the mathematical competencies associated with data-intensive occupations.

The framework combines structured curriculum data, an externally defined occupational benchmark, and ontology-constrained large language model mapping. Alignment is examined across multiple levels, including courses, mathematical domains, learning objectives, and mathematical entities. This multilevel approach makes it possible to identify both areas of curricular strength and potential gaps between classroom learning expectations and emerging workforce practices.

The study demonstrates how generative AI can support systematic and scalable curriculum analysis while preserving transparent analytical boundaries. The framework is intended to assist educators, curriculum designers, and educational researchers in evaluating how existing mathematics pathways may respond to the changing demands of the AI era.

Key Contributions

  1. Introduces a generative-AI-assisted framework for auditing curriculum–industry alignment.
  2. Connects a five-course high school mathematics pathway with an externally defined benchmark of data-intensive occupational competencies.
  3. Examines alignment at the course, domain, learning-objective, and mathematical-entity levels.
  4. Demonstrates how ontology constraints and structured filtering can improve the transparency and interpretability of AI-assisted curriculum analysis.
  5. Provides a transferable framework that may support curriculum review and instructional planning in other educational contexts.

Conference Information

Accepted for poster presentation at the Chinese Institute of Engineers - USA, Greater New York Chapter (CIE-USA GNYC) 2026 Annual Convention, scheduled for October 25, 2026, in Flushing, New York.

Keywords

Generative Artificial Intelligence · Curriculum–Industry Alignment · Mathematics Education · Educational Data Mining · Learning Analytics · Workforce Preparation · Curriculum Evaluation