AI SEO Statistics: Tutoring Center (2026-07 edition)

Reading the 45 answers model by model shows how differently the three assistants treat the same tutoring center questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 86.7% (ChatGPT) down to 60% (Claude), a 27-point gap on an identical question set.

Across the 15 tutoring center answers it produced, ChatGPT recommended hiring a professional in 86.7% of them and suggested a DIY approach first 20% of the time. It named a specific provider in 40% of answers (about 1.2 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 40%, and told the buyer to verify credentials in 46.7%, averaging 546 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 20%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Across the 15 tutoring center answers it produced, Claude recommended hiring a professional in 60% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 53.3% of answers (about 1.5 distinct providers per answer) and included price or cost information 26.7% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 33.3%, and told the buyer to verify credentials in 26.7%, averaging 283 words per answer. On the remaining cues it told the buyer to check reviews in 26.7%, pointed to case studies or a portfolio in 6.7%, and framed the choice around local proximity in 33.3%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 tutoring center answers it produced, Gemini recommended hiring a professional in 60% of them and suggested a DIY approach first 0% of the time. It named a specific provider in 46.7% of answers (about 2.1 distinct providers per answer) and included price or cost information 33.3% 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 6.7%, averaging 256 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 53.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a tutoring center buyer to a professional (86.7%) and Claude the least (60%). ChatGPT produced the longest answers, at 546 words on average. Specific providers were named most often by Claude (53.3%) — even there, roughly one answer in 2 carried a name.

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

• Mentions local proximity: 33.3%–40% across all three (a 7-point spread).
• Gives selection criteria: 53.3%–60% across all three (a 7-point spread).
• Recommends multiple quotes: 0%–6.7% across all three (a 7-point spread).
• Names a specific provider: 40%–53.3% across all three (a 13-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "recommends multiple quotes" (identical coding in 93.3% of questions) and least consistently on "asks a clarifying question" (20%).

Beyond whether to hire, the rubric codes how carefully each assistant protects the tutoring center buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 17.8% of answers on average. Verifying credentials or certifications appeared in 26.7%. Warning about red flags or scams appeared in 28.9%.

On structuring the decision, a selection-criteria checklist showed up in 55.5% of answers on average and a recommendation to gather multiple quotes in 2.2%. The single least-reproduced protective signal for tutoring center is "recommends multiple quotes" at 2.2% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

The 15 questions behind every percentage on this page were drawn from real tutoring center (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 tutoring center 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.