DOI: 10.18738/t8/bjhxsg
title: Degree_industry_enrollment_readiness_with_students_at_risk

description: &lt;section&gt;
 &lt;h1&gt;Degree vs Destiny: Mapping the Gap Between Academic Training and the AI-Integrated Job Market&lt;/h1&gt;

 &lt;h2&gt;Project Overview&lt;/h2&gt;
 &lt;p&gt;
 This project analyzes how well Texas bachelor’s degree programs prepare students for an AI-driven workforce.
 We used public datasets and a deterministic, rules-based Python pipeline to identify students at risk of
 graduating into high-exposure occupations without AI curriculum support.
 &lt;/p&gt;

 &lt;p&gt;&lt;strong&gt;Objectives:&lt;/strong&gt;&lt;/p&gt;
 &lt;ul&gt;
 &lt;li&gt;Identify students enrolled in high-exposure programs at universities lacking AI integration.&lt;/li&gt;
 &lt;li&gt;Highlight gaps in university readiness for AI-driven labor market changes.&lt;/li&gt;
 &lt;li&gt;Provide actionable insights for universities, advisors, and policymakers.&lt;/li&gt;
 &lt;/ul&gt;

 &lt;hr /&gt;

 &lt;h2&gt;Datasets&lt;/h2&gt;
 &lt;table&gt;
 &lt;thead&gt;
 &lt;tr&gt;
 &lt;th&gt;Source&lt;/th&gt;
 &lt;th&gt;Data Used&lt;/th&gt;
 &lt;th&gt;Purpose&lt;/th&gt;
 &lt;/tr&gt;
 &lt;/thead&gt;
 &lt;tbody&gt;
 &lt;tr&gt;
 &lt;td&gt;&lt;strong&gt;Census PSEO&lt;/strong&gt;&lt;/td&gt;
 &lt;td&gt;Bachelor’s program records, median earnings (1- and 5-year), post-graduation industry flows&lt;/td&gt;
 &lt;td&gt;Map degrees to workforce outcomes&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;&lt;strong&gt;NCES 2020 CIP–SOC Crosswalk&lt;/strong&gt;&lt;/td&gt;
 &lt;td&gt;CIP codes linked to SOC occupations&lt;/td&gt;
 &lt;td&gt;Translate academic majors into likely careers&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;&lt;strong&gt;O*NET&lt;/strong&gt;&lt;/td&gt;
 &lt;td&gt;Occupation-level AI Exposure Scores (0–10)&lt;/td&gt;
 &lt;td&gt;Measure automation risk for each occupation&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;&lt;strong&gt;THECB Enrollment Data&lt;/strong&gt;&lt;/td&gt;
 &lt;td&gt;2025 enrollment counts for 10 major Texas public universities&lt;/td&gt;
 &lt;td&gt;Count students per program&lt;/td&gt;
 &lt;/tr&gt;
 &lt;tr&gt;
 &lt;td&gt;&lt;strong&gt;Manual Readiness Audit&lt;/strong&gt;&lt;/td&gt;
 &lt;td&gt;Review of 2025–2026 course catalogs&lt;/td&gt;
 &lt;td&gt;Label programs as &lt;code&gt;READY&lt;/code&gt; or &lt;code&gt;NOT READY&lt;/code&gt; for AI curriculum&lt;/td&gt;
 &lt;/tr&gt;
 &lt;/tbody&gt;
 &lt;/table&gt;

 &lt;hr /&gt;

 &lt;h2&gt;Methodology&lt;/h2&gt;
 &lt;p&gt;All analysis was deterministic and rules-based using Python (Pandas). No machine learning was used.&lt;/p&gt;
 &lt;ol&gt;
 &lt;li&gt;&lt;strong&gt;Data Standardization:&lt;/strong&gt; Filtered bachelor’s programs and standardized CIP codes.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Degree-to-Occupation Mapping:&lt;/strong&gt; Linked each major to its primary occupation via the NCES crosswalk.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;AI Exposure Assignment:&lt;/strong&gt; Merged O*NET AI Exposure Scores for each occupation.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Risk Classification:&lt;/strong&gt; Occupations with AI exposure &ge; 6.5 labeled &lt;code&gt;High Exposure&lt;/code&gt;.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Enrollment Integration:&lt;/strong&gt; Added THECB enrollment counts per program.&lt;/li&gt;
 &lt;li&gt;
 &lt;strong&gt;Curriculum Readiness Audit:&lt;/strong&gt; Programs labeled &lt;code&gt;READY&lt;/code&gt; if AI coursework or certificates exist,
 otherwise &lt;code&gt;NOT READY&lt;/code&gt;.
 &lt;/li&gt;
 &lt;li&gt;
 &lt;strong&gt;Final Metric – Students at Risk:&lt;/strong&gt; Students in programs where AI Exposure &ge; 6.5 &lt;strong&gt;and&lt;/strong&gt;
 Institutional Status = &lt;code&gt;NOT READY&lt;/code&gt;.
 &lt;/li&gt;
 &lt;/ol&gt;

 &lt;hr /&gt;

 &lt;h2&gt;Results&lt;/h2&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;strong&gt;At Risk Students:&lt;/strong&gt; ~33,000 students (19.4% of the sample) are in high-exposure programs at Not Ready institutions.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;AI Exposure Distribution:&lt;/strong&gt; Scores range from 2 to 9, mean ~5.7. High exposure is concentrated in Business, Communication, and Liberal Arts majors.&lt;/li&gt;
 &lt;/ul&gt;

 &lt;hr /&gt;

 &lt;h2&gt;Key Findings&lt;/h2&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;strong&gt;Readiness Divide:&lt;/strong&gt; Tier 1 universities like UT Austin and Texas A&amp;M have integrated AI literacy, while others remain unprepared.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Critical Risk at TXST and SHSU:&lt;/strong&gt; High enrollment in high-exposure programs without AI curriculum.&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Structural Misalignment:&lt;/strong&gt; Degree programs are still oriented toward the 2020 labor market, leaving students unprepared for AI-driven 2026 workflows.&lt;/li&gt;
 &lt;/ul&gt;

 &lt;hr /&gt;

 &lt;h2&gt;Implications&lt;/h2&gt;
 &lt;ul&gt;
 &lt;li&gt;Universities should integrate AI literacy and practical AI tools across high-exposure majors.&lt;/li&gt;
 &lt;li&gt;Institutional leaders can use at-risk counts to prioritize AI coursework and curriculum updates.&lt;/li&gt;
 &lt;li&gt;Academic advisors can guide students toward complementary skills, minors, or micro-credentials.&lt;/li&gt;
 &lt;li&gt;Policymakers can target support toward institutions with the largest gaps between AI exposure and curriculum readiness.&lt;/li&gt;
 &lt;/ul&gt;

 &lt;hr /&gt;

 &lt;h2&gt;Files&lt;/h2&gt;
 &lt;ul&gt;
 &lt;li&gt;&lt;strong&gt;Dataset&lt;/strong&gt; – Final merged dataset used for analysis&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;data_dictionary.tab&lt;/strong&gt; – Definitions and descriptions of all variables in the dataset&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;Mapping the Gap Between Academic Training and the AI Integrated Job Market&lt;/strong&gt; – Project poster&lt;/li&gt;
 &lt;li&gt;&lt;strong&gt;README.md&lt;/strong&gt; – This file&lt;/li&gt;
 &lt;/ul&gt;
&lt;/section&gt;