GRM-041 AI-Driven Analysis of OpenALG Curriculum: Mapping AI Competencies Across Georgia’s Higher Education Landscape

This project investigates the presence of artificial intelligence (AI) competencies across Georgia’s higher education curriculum using university course catalogs as the primary data source, supplemented by OpenALG materials. We applied large language models, including OpenAI’s ChatGPT and embedding APIs, to analyze over 34,000 courses summarizing content, classifying AI relevance, and mapping to global frameworks (AI4K12 and UNESCO). Techniques such as topic clustering, semantic similarity analysis, and geographic distribution mapping were used to uncover patterns in AI integration. Findings reveal that AI content is concentrated in computing disciplines and research universities, with limited coverage in community colleges, MSIs, and non-technical fields. The results highlight the need for more equitable and interdisciplinary AI education across Georgia’s institutions.

Publication Details

Journal
DigitalCommons - Kennesaw State University (Kennesaw State University)
Published
2025-04-15
Primary Topic
Saffron Plant Research Studies
Type
article
Field-Weighted Citation Impact
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article

GRM-041 AI-Driven Analysis of OpenALG Curriculum: Mapping AI Competencies Across Georgia’s Higher Education Landscape

DigitalCommons - Kennesaw State University (Kennesaw State University)
Saffron Plant Research Studies
article

GRM-041 AI-Driven Analysis of OpenALG Curriculum: Mapping AI Competencies Across Georgia’s Higher Education Landscape

article en

Abstract

This project investigates the presence of artificial intelligence (AI) competencies across Georgia’s higher education curriculum using university course catalogs as the primary data source, supplemented by OpenALG materials. We applied large language models, including OpenAI’s ChatGPT and embedding APIs, to analyze over 34,000 courses summarizing content, classifying AI relevance, and mapping to global frameworks (AI4K12 and UNESCO). Techniques such as topic clustering, semantic similarity analysis, and geographic distribution mapping were used to uncover patterns in AI integration. Findings reveal that AI content is concentrated in computing disciplines and research universities, with limited coverage in community colleges, MSIs, and non-technical fields. The results highlight the need for more equitable and interdisciplinary AI education across Georgia’s institutions.

DigitalCommons - Kennesaw State University (Kennesaw State University)
Quality Education
Openalex Percentile: Top 38%
Saffron Plant Research Studies
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