Degree_industry_enrollment_readiness_with_students_at_risk

<section>
<h1>Degree vs Destiny: Mapping the Gap Between Academic Training and the AI-Integrated Job Market</h1>

<h2>Project Overview</h2>
<p>
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.
</p>

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

<hr />

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

<hr />

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

<hr />

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

<hr />

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

<hr />

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

<hr />

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