Mapping AI Programs in the U.S: A Status Report from Early 2026 and an Analysis of AI Majors and Minors

2026-05-05 · Source: cs.AI updates on arXiv.org · Field: Education & Learning — Academic Research & Higher Education, Educational Technology (EdTech), Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

A new report details the status of undergraduate Artificial Intelligence (AI) programs in the U.S. as of Spring 2026, based on data from an automated scraping and mapping tool available at "cicmap.ai". This tool surveyed over 560 institutions, representing 86% of all U.S. Computer Science (CS) graduates, identifying more than 350 AI programs including majors, minors, concentrations, and certificates. The analysis found that 249 out of 569 (44%) surveyed schools offer at least one AI program, with concentrations being the most common type (32.7%). For 66 AI majors analyzed, 92% require a general AI course or an ML course, and 37.9% mandate an Ethics in AI course. Among 87 AI minors, 78.2% require a general AI course, but only 24.1% include an ethics course. The study highlights significant variability in major credit requirements and a more standardized structure for minors.

Key takeaway

For prospective AI students researching undergraduate programs, utilize "cicmap.ai" to compare program requirements and ethical course integration across U.S. universities. If you are a university administrator developing or refining AI curricula, consider the prevalence of general AI and ML courses, and the lower inclusion of ethics in minor programs, to ensure your offerings meet evolving educational standards and student demand. This tool provides a critical benchmark for program design.

Key insights

The U.S. AI education landscape in 2026 is diverse, with a new tool mapping programs and revealing varied course requirements.

Principles

• AI education is rapidly expanding.
• Program requirements vary significantly.
• Ethics courses are less common in minors.

Method

A human-in-the-loop scraping pipeline uses Google/Exa search APIs and DeepSeek LLM to identify university and departmental websites, then extracts program types and course requirements, validated by manual review.

In practice

• Explore "cicmap.ai" for program details.
• Benchmark AI curricula against peers.
• Inform new AI program development.

Topics

• AI Education
• Undergraduate Programs
• Data Scraping
• AI Curriculum
• Machine Learning
• Ethics in AI

Code references

• muzny/cicmap-april2026-snapshot-paper

Best for: Research Scientist, AI Student, Consultant

Related on AIssential

• The AI industry is still in flux, and university programs are trying to keep up - marketplace.org — Rapid AI evolution necessitates continuous curriculum updates and a focus on foundational principles in higher education.
• AI skills supply and demand — EU AI education aligns with job demand, but non-ICT sectors and emerging AI show potential future skill gaps.
• [D] Self-Promotion Thread — The community is actively developing diverse open-source tools and frameworks for ML education, research, and practical applications.
• Test: Test zzzAdministrator Wed, 12/17/2025 - 07:41 Test Testerson Tester 123-432-2313 — CMU's AI portal highlights its historical contributions, current research, and educational programs in artificial intelligence.
• 95% of UK students now use AI and their experiences couldn't be more divided — Widespread AI adoption among UK students creates a critical gap in university support and raises concerns about skill development.
• Forthcoming machine learning and AI seminars: March 2026 edition — AIhub provides a centralized, free resource for upcoming virtual AI and machine learning seminars.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.