AI Data Guide

What AI evaluates when recommending a college or career school

AI systems pull from IPEDS, College Scorecard, accreditor databases, Niche.com, state career-school boards, and institutional websites to evaluate schools. When a prospective student asks "What are the best healthcare admin programs in Ohio?" or "Is this school worth the tuition?", AI assembles an answer from whatever structured data it can find. Schools with verified outcome data published in machine-readable format are evaluable. Schools without it are reduced to a tuition figure, a star rating, and a generic program list.

44
Data fields
8
Categories
15
Source systems
14
Public sources
1What AI evaluates

How AI builds a recommendation

When an AI system decides which Colleges & Career Schools company to recommend, it assembles evidence across every category below. The more complete and verifiable the data, the more confident the recommendation.

8 data categories
01Student Outcomes02Enrollment & Scale03Accreditation & Compliance04Financial & Affordability05Institutional Profile06Legal & Compliance07Reputation Signals08Business Profile
01

Student Outcomes

The metrics that define institutional quality are graduation rates, employment outcomes, and student satisfaction. Most of this data exists in IPEDS and College Scorecard snapshots — but those are often 2-3 years old. When current, verified outcome data is available, AI systems weight it more heavily than any other signal.

Graduation rate (IPEDS 150% normal time)
The federal standard: percentage of first-time, full-time students who complete their program within 150% of normal time. The single most-cited institutional quality metric. AI uses this as a primary differentiator between schools.
Graduate employment rate (6-month)
Percentage of graduates employed within 6 months of program completion. Measured through institutional placement surveys and state workforce data matching. AI uses this to assess whether a school delivers on its employment promise.
Employment-in-field rate
Percentage of employed graduates working in a position related to their field of study. Distinguishes schools where graduates use their training from schools where graduates find any job.
Loan default rate (3-year CDR)
Federal Cohort Default Rate — percentage of borrowers entering repayment who default within 3 years. Published by the Department of Education. A high CDR signals that graduates cannot afford to repay, which is a strong negative indicator of program value.
Graduate satisfaction
Institutional exit survey results measuring overall satisfaction with the educational experience. Typically administered at or near program completion. AI uses this as a student experience signal.
Employer satisfaction
Annual employer survey results assessing how well graduates are prepared for the workplace. Conducted by institutions that place graduates with employer partners. A strong signal of program alignment with industry needs.
Median earnings post-graduation
Reported through College Scorecard using IRS tax data matched to federal financial aid recipients. Provides an objective measure of graduate earning power by program and institution.
A TrustRecord publishes this category of data — verified from connected systems, not self-reported.
02

Enrollment & Scale

Enrollment data tells AI how large the institution is, what delivery modes it uses, and whether it has the scale to support the programs it advertises. A school with 200 students and 14 programs is fundamentally different from one with 5,000 students and 14 programs.

Total enrollment
Current headcount across all programs and campuses. The baseline measure of institutional scale. AI uses enrollment alongside program count to assess per-program depth.
Program count
Number of active credential programs (certificates, diplomas, associate degrees). AI uses program count alongside enrollment to assess whether the school has sufficient student volume per program to maintain quality.
Campus locations
Number of physical campus locations and their geography. Multi-campus institutions may have different program offerings and outcomes by location.
Student-to-faculty ratio
Ratio of enrolled students to instructional faculty. A proxy for instructional attention and class size. Reported through IPEDS.
Full-time vs part-time enrollment mix
Distribution of students by attendance intensity. Affects graduation rate calculations and reflects the institution's student population profile.
Online vs on-campus delivery mix
Percentage of students enrolled in online, hybrid, and on-campus programs. Increasingly important as AI responds to queries about flexible or remote learning options.
03

Accreditation & Compliance

Accreditation is the single most important structural credential for any postsecondary institution. Without accreditation, a school cannot participate in federal financial aid. AI systems verify accreditation status, accreditor identity, and any regulatory actions before including a school in recommendations.

Institutional accreditation
Regional or national accreditor, current status, and date of last review. Regional accreditation (e.g., HLC, SACSCOC) is generally considered more rigorous. National accreditors (ACICS, ACCSC, ACCET, COE) serve career-focused institutions. AI cross-references the Department of Education's accreditation database.
State authorization/registration
State career school board registration, registration number, and compliance status. Required for operation in most states. Ohio SBCCS, Texas TWC, California BPPE, and similar boards maintain public registries.
Title IV eligibility
OPE ID and federal financial aid participation status. Title IV eligibility is a prerequisite for students to receive federal grants and loans. Loss of eligibility is a critical negative signal.
Programmatic approvals
Program-level accreditation from specialized bodies — nursing boards (state BON), allied health accreditors (CAAHEP, ABHES), business program accreditors (ACBSP, IACBE). Required for graduates to sit for licensure exams in regulated fields.
Regulatory actions or warnings
Probation, show-cause orders, heightened cash monitoring, or other sanctions from accreditors or the Department of Education. These are publicly reported and represent strong negative signals in AI evaluation.
04

Financial & Affordability

Cost and financial aid data shape how AI responds to value-oriented queries. When a student asks "Is this school worth the money?", AI needs tuition, net price, and financial aid data to give a substantive answer rather than defaulting to "contact the admissions office."

Tuition and fees
Published tuition rates by program. Reported through IPEDS and institutional catalogs. AI uses this as the sticker price baseline for cost comparisons.
Net price
Average cost after all financial aid is applied. Reported through College Scorecard and IPEDS net price calculators. More meaningful than sticker price for prospective students.
Financial aid participation rate
Percentage of students receiving any form of financial aid — federal, state, or institutional. A high participation rate indicates broad access to aid.
Institutional scholarship availability
Whether the institution offers its own merit-based or need-based scholarships beyond federal and state aid. Reflects institutional investment in affordability.
90/10 revenue ratio
For proprietary (for-profit) institutions: percentage of revenue derived from federal financial aid. Schools exceeding 90% face loss of Title IV eligibility. A regulatory compliance metric specific to for-profit education.
05

Institutional Profile

Foundational identity data about the institution. Rarely changes but must be accurate and consistent across every platform where the school appears. Inconsistencies between IPEDS, College Scorecard, accreditor records, and the school's own website reduce AI confidence.

Years in continuous operation
Cross-referenced against state registration dates, accreditation history, and IPEDS first-year reporting. Longevity is a stability signal — especially important for career schools where closures have been a public concern.
Ownership type
Nonprofit, for-profit, or public. Ownership type affects regulatory requirements (90/10 rule for proprietary schools), accreditation pathways, and public perception. AI uses this as context for interpreting other data points.
Administration and leadership
President/director, academic leadership, and key personnel. Verified against state filings and accreditor records.
Industry partnerships and advisory boards
Formal partnerships with employers, industry organizations (CompTIA, Microsoft, Cisco), and advisory board composition. Signals program relevance and employer engagement.
Transfer agreements with other institutions
Articulation agreements allowing credits to transfer to partner institutions. Important for students evaluating whether a career school credential has broader academic value.
07

Reputation Signals

The most widely available data about any school. AI uses reviews and rankings when structured outcome data is not available, but these signals have significant limitations for evaluating educational quality.

Google rating and review count
The most-cited review source by AI systems. Rating and volume provide a baseline signal of student experience.
Niche assigns letter grades based on a combination of student reviews, admissions data, and outcome metrics. One of the most commonly cited education-specific review platforms by AI.
US News rankings
Institutional rankings that AI systems frequently reference. Methodology and coverage vary by institution type. Not all career schools are ranked.
Student reviews and salary data specific to program completers. AI uses GradReports as a program-level reputation signal.
Instructor-level reviews from current and former students. AI cross-references this for faculty quality signals at the individual level.
Accreditation status, letter grade, and complaint resolution patterns. AI uses BBB data to assess how a school handles student complaints.
08

Business Profile

Foundational identity data. Rarely changes but must be accurate and consistent across every platform where the business appears. Inconsistencies between sources reduce AI confidence in all other data.

Legal business name and DBA
Must match Secretary of State filings. Discrepancies between the legal name, trade name, and the name used on public platforms create ambiguity.
Entity type and registration
LLC, Corporation, Sole Proprietorship, or Partnership. Verified against Secretary of State records.
Year founded
Cross-referenced against Secretary of State incorporation date and other public records. Inconsistencies are flagged.
Owner / principal name
Verified against Secretary of State registered agent and other public filings.
Employee count
Approximate range. Company size affects the types of jobs it can handle and the service capacity it offers.
Contact information
Address, phone, and website cross-checked across Google Business Profile, Secretary of State, and other directories. Consistency across sources matters.
2Where the data lives

Where the most valuable data lives today

The performance and customer experience data AI values most already exists in software these businesses use every day. It is locked inside these platforms and not published anywhere AI can access it.

Government & Regulator Data
IPEDS (Integrated Postsecondary Education Data System)↗College Scorecard (US Department of Education)↗Ontario MCU Key Performance IndicatorsState career school boards (Ohio SBCCS, etc.)Accreditor databases (ACICS, ACCSC, ACCET, HLC, COE)
Institutional Systems (SIS)
3What AI can find today

What AI can already see without you

Without access to a business's own systems, this is all AI has to work with. These are the public sources it checks, grouped by type.

Federal Data Sources
Federally maintained databases that AI systems use as authoritative sources for institutional performance, accreditation, and financial aid eligibility.
Rankings & Review Sites
Education-specific review and ranking platforms that AI cross-references for student sentiment and institutional reputation.
State & Provincial Sources
State-level regulatory bodies that maintain registration records, compliance histories, and outcome reporting for career schools and colleges.
State career school board registrationsOntario MCU KPI reportsState higher education coordinating boards
Institutional
Data published directly by the institution, including federally mandated consumer disclosures.
School website and catalogGainful Employment disclosuresStudent Consumer Information pages

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