REVIEW 1 major objections 1 minor
A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors
T0 review · 1 major / 1 minor · reviewed 2026-06-30 · grok-4.3
Pith's one-line read A 2026 mapping of US undergraduate AI programs finds high variability in requirements, with majors needing either a general AI or ML course and ethics included in more than a third of majors versus under a quarter of minors.
desk verdict A practical snapshot and public tool for tracking US AI undergrad programs, but the course requirement takeaways rest on unvalidated scraping with no accuracy checks. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The scraping and mapping tool that detects, extracts, and displays course requirements from university websites to produce an updatable inventory of AI programs.
What would settle it
A manual audit of university websites that finds multiple AI programs or specific course requirements the tool missed or misclassified.
Extended reading notes
Core claim
The paper establishes a historic record of AI programs in the United States in early 2026 through systematic scraping of university websites, locating more than 350 undergraduate programs at 560 institutions. Analysis of the 66 majors and 87 minors shows substantial variation in the number and type of required courses, but reveals that every major requires either a general AI course or a machine learning course and that ethics in AI is mandated in more than a third of majors yet in just under a quarter of minors.
Load-bearing premise
The scraping tool correctly detects, classifies, and extracts accurate course requirements from university websites for all relevant programs without significant omissions, misclassifications, or outdated information across the 560 institutions sampled.
Editorial extensions
If this is right
- AI majors and minors differ substantially in total credit hours and specific course mandates across institutions.
- Machine learning functions as the required substitute whenever a general AI course is absent from a major.
- Ethics in AI appears as a required course more often in majors than in minors.
- The inventory supplies a continually updated resource for comparing program requirements.
Reading between the lines
- The observed differences suggest that students completing only a minor may receive less exposure to AI ethics than those completing a major.
- Continued growth in new programs will require ongoing updates to keep the inventory current.
- The high coverage of institutions producing most CS graduates makes the patterns representative for the majority of computing students.
- Variability in requirements could prompt discussion of whether some standardization of core AI and ethics content would benefit students transferring between institutions.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper describes a web-scraping tool and public interface (cicmap.ai) that searched 560 U.S. four-year institutions (covering 86% of CS graduates) to identify more than 350 undergraduate AI programs (majors, minors, concentrations, certificates) as of early 2026. It supplies a snapshot of these programs and analyzes course requirements across 66 AI majors and 87 AI minors, reporting high variability in program size and two quantitative findings: every major without a general AI course requires an ML course instead, and more than one-third of majors but fewer than one-quarter of minors require an Ethics in AI course.
Significance. If the data extraction proves reliable, the work supplies a timely public resource and historic record of AI education during rapid growth, with the continually updating cicmap.ai tool as a concrete contribution that could aid students, advisors, and administrators. The scale of the institutional sample is a strength.
major comments (1)
- [Methods / scraping tool description and analysis of majors/minors] The description of the scraping process and cicmap.ai interface (abstract and methods) reports no validation of detection, classification, or extraction accuracy—no precision/recall figures, no inter-rater reliability, no manual audit of a sample of catalogs against source pages, and no discussion of deduplication or filtering rules. This directly undermines the two central quantitative claims in the analysis of majors and minors, which rest on correct identification of AI, ML, and Ethics course presence/absence across all 66 majors and 87 minors.
minor comments (1)
- [Abstract] The abstract states the tool 'dynamically update[s]' but provides no details on update frequency, handling of catalog changes, or how users can verify current data.
Simulated Author's Rebuttal
We thank the referee for their constructive feedback and positive assessment of the work's timeliness, scale, and potential utility as a public resource. The major comment identifies a clear gap in the methods description regarding validation and procedural details, which we will address in revision. Below we respond point by point.
read point-by-point responses
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Referee: [Methods / scraping tool description and analysis of majors/minors] The description of the scraping process and cicmap.ai interface (abstract and methods) reports no validation of detection, classification, or extraction accuracy—no precision/recall figures, no inter-rater reliability, no manual audit of a sample of catalogs against source pages, and no discussion of deduplication or filtering rules. This directly undermines the two central quantitative claims in the analysis of majors and minors, which rest on correct identification of AI, ML, and Ethics course presence/absence across all 66 majors and 87 minors.
Authors: We agree this is a substantive omission. The manuscript does not report validation metrics or detail the deduplication and filtering rules, which weakens confidence in the reported counts and the two quantitative takeaways. In the revised manuscript we will add a new Methods subsection that (a) explicitly describes the deduplication logic (unique keys combining institution, program title, and catalog URL) and filtering criteria (undergraduate-only, AI-focused programs at four-year institutions), and (b) presents results from a manual audit of a random sample of 50 institutions. The audit will compare tool outputs against direct catalog inspection and report precision, recall, and F1 scores for both program detection and course-type classification. If inter-rater checks were performed during tool development they will be reported; otherwise we will note that limitation and describe the single-reviewer protocol used. These additions will directly support the reliability of the 66-major / 87-minor analysis. revision: yes
Circularity Check
No circularity: purely descriptive survey of scraped program data with no derivations or self-referential reductions
full rationale
The manuscript is a status report that scrapes and tabulates course requirements from university websites for 66 majors and 87 minors. It contains no equations, fitted parameters, predictions, or uniqueness theorems. The two takeaways are direct empirical counts (presence/absence of AI, ML, and Ethics courses) extracted from the data; they do not reduce to any prior fitted value or self-citation. The scraping tool is described as a methodological contribution but is not invoked in a way that makes the reported fractions circular. No self-citation load-bearing steps, ansatzes, or renamings of known results appear. The work is self-contained as a descriptive snapshot.
Assumptions & free parameters
assumptions (1)
- domain assumption The web scraping tool accurately detects, classifies, and extracts course requirements from university websites without significant omissions or errors across the sampled institutions.
Cite this review
Pith. "Pith review of A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors." pith.science (2026). https://pith.science/paper/IJBOEKEM
@misc{pith2026260612428,
author = {Pith},
title = {Pith review of: A Tool to Map AI Programs in the U.S.: A Snapshot from April 2026 and an Analysis of Requirements for AI Majors and Minors},
year = {2026},
howpublished = {\url{https://pith.science/paper/IJBOEKEM}},
note = {Machine review of arXiv:2606.12428}
}
read the original abstract
In this work, we locate and analyze existing undergraduate Artificial Intelligence (AI) programs in the United States in Spring 2026, creating a historic record at a time of great change in this area. To create this record, we developed a tool to detect, scrape, and display data from 361 undergraduate AI programs--majors, minors, concentrations, and certificates--at 4-year universities. Our tool, available at https://cicmap.ai, searched 563 institutions to locate these programs, a sample that represents 87% of all undergraduate Computer Science (CS) graduates in the U.S in 2025. This tool allows prospective students, guidance counselors, administrators, and faculty to easily access AI program requirements and is designed to continually update as new programs emerge. To the best of our knowledge, this survey represents the most comprehensive snapshot of the state of AI programs in the U.S. to date. With this work we offer three important contributions: 1) a record of AI programs in the U.S. at a time of great upheaval; 2) a tool to explore AI programs and their requirements; and 3) an analysis of the courses required for 66 AI majors and 87 AI minors. Our analysis of majors and minors shows great variability in the size and the requirements of these degrees, but we note two takeaways. First, not all majors require a general AI course, but if they don't, they do require a Machine Learning (ML) course. Second, more than a third of majors require an Ethics in AI course but only 24% of minors do.
Figures
Reviewed June 30, 2026 · model on record in the stance chip above.
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