This article will guide you on how to create a teacher survey about teacher mentoring. With Specific, you can generate such a survey in seconds, saving energy and capturing better insights.
Steps to create a survey for teachers about teacher mentoring
If you want to save time, just generate a survey with Specific in a few clicks. Here’s how easy it is:
Tell what survey you want.
Done.
That’s it. You truly don’t even need to read further if speed is your goal. AI handles the expert knowledge so you get quality, relevant questions instantly. Even more, each respondent can get contextual follow-up questions in real time, ensuring you gather deeper mentoring insights than with static forms. If you want a completely custom survey or want to start from scratch, try the AI survey generator—no expertise required.
Why run a teacher mentoring survey?
Teacher mentoring isn’t just a checkbox—it directly shapes satisfaction, performance, and retention. Here’s the reality: mentored teachers had an attrition rate of only 5%, compared to 18% for those without mentors [1]. That’s a huge gap. If you’re not running these surveys, you’re missing out on:
Pinpointing what works and what blocks results in your mentoring programs
Spotting professional needs and gaps before great teachers leave
Cultivating a school culture where teachers feel heard and supported
It’s not a minor thing. With 40-50% of teachers leaving the profession within the first five years [2], feedback on mentoring isn’t just helpful—it’s what helps you keep good people in the classroom. When you prioritize the importance of teacher recognition and feedback, you support educators and inform your school’s decision-making at the same time.
What makes a good teacher mentoring survey?
Effective surveys aren’t just lists of questions. They’re clear, focused, and inviting—engineered for both quantity and quality of responses. Let’s break that down:
Clarity and Bias-Free Design: Good surveys use plain language, avoid jargon, and don’t “lead” the respondent to feel a certain way.
Conversational Tone: When a survey feels like a discussion rather than a checklist, teachers actually open up and share honest reflections.
A quick visual comparison makes it plain:
Bad practices | Good practices |
|---|---|
Overly complex/technical phrasing | Simple, direct questions (“What support do you wish you had?”) |
Biased prompts (“You love your mentor, right?”) | Neutral wording (“How would you describe your mentor relationship?”) |
Too many required fields | Optional follow-ups, giving teachers control |
List-only forms | Conversational, dynamic sequencing |
A good measure of survey quality is simple: collect as many high-quality, actionable responses as you can. A conversational approach using AI can help boost both counts.
Choosing the right questions for a teacher survey about teacher mentoring
Survey design is an art—balancing structure with depth, and making sure teachers have room to explain their experience. For this topic, here’s how to use different question types effectively:
Open-ended questions let teachers express themselves in their own words—crucial for surfacing unexpected issues, stories, or innovative suggestions. Use them when you want details, emotions, or context you hadn’t considered. Example questions:
“Describe a positive mentoring experience you’ve had this year.”
“What’s one thing you wish your mentor did differently?”
Single-select multiple-choice questions give you structured data that’s faster to analyze, perfect for when you want to measure prevalence or identify patterns. Example:
How often do you meet with your mentor?
Weekly
Monthly
Less often / Only as needed
NPS (Net Promoter Score) question is ideal for benchmarking mentor program effectiveness and tracking improvement over time. If you want to explore this question type, generate a custom NPS survey for teachers about teacher mentoring with one click. Example:
How likely are you to recommend the teacher mentoring program to a new teacher? (0 = Not at all likely, 10 = Extremely likely)
Followup questions to uncover “the why” are your secret weapon. When a teacher makes a statement (“I wish I had more support at the start”), you want to dig deeper. That’s where AI-powered followups shine—helping you clarify, understand, and really get to the why. Example:
Initial response: “My mentor helped me with lesson planning.”
AI follow-up: “Can you share a specific example of how your mentor’s support made a difference?”
Want more inspiration, sample questions, or tips on effective survey composition? Check out these best questions for teacher survey about mentoring—it helps to see what works in practice!
What is a conversational survey?
In a nutshell, a conversational survey feels like a real back-and-forth—not a stale form. Instead of bottling up feedback with restrictive formats, the survey adapts, asks smart followups, and matches the tone you want.
How is an AI survey generator different? Think of it as having an expert researcher by your side: you give a prompt, and the AI instantly composes clear, nuanced questions, even suggesting follow-ups depending on teacher replies. The AI survey editor lets you refine in plain language, fine-tuning as you go. All that friction and manual editing in Typeform or Google Forms? Gone.
Manual surveys | AI-generated (conversational) surveys |
|---|---|
Static, one-size-fits-all | Adaptive, personalized conversations |
Time-consuming to build | Ready in seconds using AI |
Limited follow-up, often unclear responses | Follow-up questions in real-time for clarity |
Bland user experience | Smooth, chat-like interactions |
Why use AI for teacher surveys? Because teachers want to share—but only if it’s easy, respectful, and feels worth their time. An AI survey example is always improving; it crafts better questions, adapts to feedback, and makes reviewing responses so much easier. That’s why Specific delivers the best-in-class experience for conversational surveys, boosting completion rates and making life easier for both survey creators and respondents. Want to dive deep on this process? Here's a full guide on how to analyze responses from teacher surveys about mentoring.
The power of follow-up questions
If you want actionable insights, you absolutely need to go beyond surface-level data. Automated follow-up questions—like those in Specific’s AI follow-up system—turn every vague answer into something useful. Instead of endlessly pinging teachers for clarification, AI does it in the flow, instantly, as if you had a personal interview with each teacher. This doesn’t just save time—it uncovers depth and context you’d otherwise miss.
Teacher: “Mentoring was helpful, but there were challenges.”
AI follow-up: “Can you explain what the main challenges were, and how they affected you?”
Without that follow-up, “helpful but challenging” tells you almost nothing. With it, you get new data to improve teacher mentoring right away.
How many followups to ask? Usually, 2-3 followups dig out the key context without overloading the respondent. In Specific, you can set this up, or let the survey move on once the main insight is collected. That keeps surveys conversational and respectful of teachers' time.
This makes it a conversational survey—dynamic, context-aware, and totally different from static forms.
AI survey response analysis is painless, too. Even when you get paragraphs of open-ended feedback, tools like Specific’s AI survey response analysis summarize and categorize everything automatically so you can act on trends fast.
Try running an AI-powered survey and see how much follow-up improves what you learn. You’ll be amazed.
See this teacher mentoring survey example now
Discover just how easy and insightful a conversational teacher mentoring survey can be—get meaningful feedback, rapid analysis, and context-rich responses with AI-driven follow-ups. Start building your survey now and experience the difference.