Original research · 2026-07 edition

AI SEO Statistics: School (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

The question bank

The questions we tested — sampled from real buyer journeys in school.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

My son is being bullied at his current public school and I need to find a private alternative that prioritizes social-emotional learning immediately.
What are the main differences between a Montessori curriculum and a traditional private school for a preschooler?
How do I know if my child would benefit more from a specialized school for dyslexia versus staying in public school with an IEP?
Is it worth paying $20k a year for private elementary school or should I save that money for their college fund instead?
What are some red flags I should look for when interviewing the principal of a potential new charter school?
I'm moving to a new city next month; how can I check the actual safety ratings and disciplinary records of local schools?
Can I find a private school that offers financial aid or sliding scale tuition for a middle-class family?
What questions should I ask current parents of a school to get the real story on the culture and teacher turnover?
Show all 15 questions
Should I hire a private education consultant to help my high schooler get into a competitive boarding school?
My daughter is gifted in music and art; are there specific types of schools that focus on performing arts for middle schoolers?
What is the typical application timeline for private schools if we want to start in the fall semester?
Are religious schools generally more academically rigorous than secular private schools in the suburbs?
How does the forest school model compare to a standard outdoor-heavy curriculum in terms of academic readiness for first grade?
We need a school with very small class sizes under 12 kids because my child gets overwhelmed easily; where do I start looking?
What are the pros and cons of an all-girls high school versus a co-ed environment for leadership development?

Model by model

18-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about school buyers.

Behavior rates across 15 school buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional33%13%7%73%
Suggests DIY first40%33%20%80%
Names specific providers7%7%13%80%
Gives price or cost info7%13%27%80%
Tells to check reviews20%13%0%67%
Tells to verify credentials13%7%0%80%
Mentions case studies / portfolio7%0%0%93%
Mentions local proximity27%20%33%47%
Gives selection criteria53%33%40%60%
Warns about red flags20%13%13%93%
Asks a clarifying question20%60%0%40%
Recommends multiple quotes13%7%0%87%

By model

How each assistant handled School questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same school questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 33.3% (ChatGPT) down to 6.7% (Gemini), a 27-point gap on an identical question set.

Across the 15 school answers it produced, ChatGPT recommended hiring a professional in 33.3% of them and suggested a DIY approach first 40% of the time. It named a specific provider in 6.7% of answers (about 0.1 distinct providers per answer) and included price or cost information 6.7% of the time. ChatGPT asked a clarifying question before answering in 20% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 13.3%, averaging 700 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 26.7%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 school answers it produced, Claude recommended hiring a professional in 13.3% of them and suggested a DIY approach first 33.3% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 60% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 6.7%, averaging 314 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 school answers it produced, Gemini recommended hiring a professional in 6.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 13.3% of answers (about 0.3 distinct providers per answer) and included price or cost information 26.7% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 13.3%, and told the buyer to verify credentials in 0%, averaging 229 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 40% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a school buyer to a professional (33.3%) and Gemini the least (6.7%). ChatGPT produced the longest answers, at 700 words on average. Specific providers were named most often by Gemini (13.3%) — even there, roughly one answer in 8 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 17.8 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a school buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 60% (Claude) — a 60-point spread.
  • Recommends hiring a professional: from 6.7% (Gemini) to 33.3% (ChatGPT) — a 27-point spread.
  • Suggests a DIY approach first: from 20% (Gemini) to 40% (ChatGPT) — a 20-point spread.
  • Gives price or cost information: from 6.7% (ChatGPT) to 26.7% (Gemini) — a 20-point spread.
  • Tells the buyer to check reviews: from 0% (Gemini) to 20% (ChatGPT) — a 20-point spread.

The widest single gap — asks a clarifying question, 60 points — means a school buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the school market.

Where they agree

The points of near-consensus in School.

On other behaviors the three models move almost in lockstep — the points of near-consensus for school, where all three landed within a few points of each other:

  • Names a specific provider: 6.7%–13.3% across all three (a 7-point spread).
  • Mentions case studies or portfolio: 0%–6.7% across all three (a 7-point spread).
  • Warns about red flags or scams: 13.3%–20% across all three (a 7-point spread).
  • Tells the buyer to verify credentials: 0%–13.3% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "mentions case studies or portfolio" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (40%).

Every behavior, measured

All twelve coded behaviors for School, averaged across the three models.

The behaviors AI models reproduce most often for school are gives selection criteria (42.2% on average), suggests a DIY approach first (31.1%) and mentions local proximity (26.7%); the rarest are mentions case studies or portfolio (2.2%), recommends multiple quotes (6.7%) and tells the buyer to verify credentials (6.7%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 42.2% on average (ChatGPT 53.3%, Claude 33.3%, Gemini 40%) — a 20-point spread.
  • Suggests a DIY approach first: 31.1% on average (ChatGPT 40%, Claude 33.3%, Gemini 20%) — a 20-point spread.
  • Mentions local proximity: 26.7% on average (ChatGPT 26.7%, Claude 20%, Gemini 33.3%) — a 13-point spread.
  • Asks a clarifying question: 26.7% on average (ChatGPT 20%, Claude 60%, Gemini 0%) — a 60-point spread.
  • Recommends hiring a professional: 17.8% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 6.7%) — a 27-point spread.
  • Gives price or cost information: 15.6% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 26.7%) — a 20-point spread.
  • Warns about red flags or scams: 15.5% on average (ChatGPT 20%, Claude 13.3%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to check reviews: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
  • Names a specific provider: 8.9% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 13.3%) — a 7-point spread.
  • Tells the buyer to verify credentials: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Recommends multiple quotes: 6.7% on average (ChatGPT 13.3%, Claude 6.7%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 2.2% on average (ChatGPT 6.7%, Claude 0%, Gemini 0%) — a 7-point spread.

Trust signals

How well the models protect the school buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the school buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.1% of answers on average. Verifying credentials or certifications appeared in 6.7%. Warning about red flags or scams appeared in 15.5%.

On structuring the decision, a selection-criteria checklist showed up in 42.2% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for school is "tells the buyer to verify credentials" at 6.7% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name School providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 school answers, a specific provider was named in 8.9% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for school: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 School questions cover.

The 15 questions behind every percentage on this page were drawn from real school (education services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact school question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific school question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →