How MSITM.com Evaluates AI Exposure
The AI Exposure label is an editorial curriculum measure based on the current published degree requirements and course options. It is not supplied or endorsed by the universities, and it is not an overall ranking of program quality.
Current 50-program distribution
| Label | Current count | Definition |
|---|---|---|
| Very High | 11 | AI is central to the degree or multiple required/core courses materially focus on AI, ML or GenAI. |
| High | 33 | Substantial AI/ML coursework, concentration options or broad AI integration. |
| Moderate | 5 | AI appears meaningfully in selected courses/electives but does not define the degree. |
| Limited / Specialized | 1 | AI appears primarily in a narrower specialized context rather than across the program. |
What the review considers
- Required/core AI and ML coursework
- AI electives and concentration depth
- Programming, data engineering and cloud foundations
- Generative AI, RAG and agentic-AI currency
- Responsible AI, governance, privacy and security
- Applied capstone, practicum or client work
Possible future numeric model — not published as a score
| Component | Conceptual weight |
|---|---|
| Required/core AI & ML | 30 |
| Electives/concentration depth | 20 |
| Programming/data/cloud | 20 |
| GenAI/agentic currency | 15 |
| Responsible AI/governance/security | 10 |
| Applied AI capstone/practicum | 5 |
MSITM.com does not currently publish these numeric AI scores. A number would imply more precision than course catalogs support. The qualitative labels are easier to audit and update when curricula change.
See all 50 AI Exposure classifications → | Curriculum & AI hub →
Methodology updated September 24, 2026.