This article will give you tips on how to analyze responses from a high school freshman student survey about mental health using AI survey analysis methods to get actionable insights.
Choosing the right tools for survey response analysis
The first step in analyzing your survey responses is understanding the kind of data you have. The approach—and tools—will depend on whether your feedback is quantitative, qualitative, or a mix of both.
Quantitative data: Think numbers—how many students chose each answer, how trends stack up. For that, tools like Excel or Google Sheets are perfect. You can quickly surface prevalence rates, like how 15% of high school students have experienced symptoms of depression [1].
Qualitative data: Open-ended responses or insights from follow-up questions are far richer, but the challenge is real: you can’t scan hundreds of text replies by hand and expect any depth. Here, AI survey tools step up—nobody has time to read and code every answer.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
If you export survey responses, you can paste the data into ChatGPT or another GPT-powered tool. This works in a pinch—ask for summaries, themes, or specific insights by chatting with the AI.
But it gets pretty clunky, fast. Pasting long strings of messy responses makes it easy to lose context. You also need to craft effective prompts for each new angle, and setting up filters or segmenting by question is extra effort. If your document is huge, you’ll hit context limits and need to chunk data manually.
All-in-one tool like Specific
Specific is an AI-powered tool built specifically for conversational survey creation and analysis. It’s not just for data collection—it’s designed to help you extract meaning from open-ended responses, at scale.
Key advantages:
While collecting data, Specific’s AI-driven follow-up questions dig deeper, so you end up with richer replies (not just “yes” or “no,” but actual context behind answers).
Once responses roll in, AI survey response analysis kicks in: the platform instantly summarizes qualitative responses, finds core ideas, and surfaces actionable insights—no spreadsheet sorting or manual coding needed.
You can chat directly with the AI about any aspect of your results, similar to ChatGPT, but with tools to filter, crop, and keep your analysis laser-focused.
This dramatically shortens the “analysis” time, so you can act quickly. Curious or want to see how it works? Check out how to analyze mental health survey responses with AI.
Useful prompts that you can use to analyze high school freshman student survey responses about mental health
Prompts are the foundation of any quality AI analysis. Here’s how I approach prompts—whether I’m working inside a tool like Specific, using ChatGPT, or experimenting with another AI.
Prompt for core ideas: This is perfect for quickly surfacing what’s really happening across a big batch of survey responses. Paste this into your AI tool:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
AI always performs better if you give more context up front—about the survey, your goals, or what your audience looks like. For example:
Analyze open-ended survey replies from high school freshman students about mental health challenges. I want to identify the main sources of stress and support mentioned by respondents. Please focus on stressors related to school, family, or social life.
Prompt for deeper exploration: Once you’ve got a core idea, simply ask:
Tell me more about “academic pressure” (or whatever core idea you want to dig into).
Prompt for specific topics: Want to check if any students talked about anxiety or lack of support?
Did anyone talk about anxiety or feeling unsupported? Include quotes.
Prompt for pain points and challenges: Especially important in the mental health context—this quickly surfaces the main frustrations or obstacles:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for sentiment analysis: Get a sense of the mood or tone (positive, negative, neutral) across all student feedback. This is useful for mapping trends against stats like, “Only about 20% of adolescents with mental health issues receive treatment” [1].
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Prompt for suggestions and ideas: Identify student-generated solutions (sometimes respondents are your best innovators):
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs and opportunities: Spotting gaps is key—maybe students are struggling but no one is talking about access to counselors:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
How Specific analyzes qualitative data by question type
Every survey question unlocks a different angle for analysis—and Specific adapts automatically:
Open-ended questions (with or without follow-ups): Specific creates a consolidated summary for all responses, including any context from automatic follow-up questions. For instance, if students elaborate on sources of stress after a basic “How are you feeling?” prompt, you’ll see every angle captured.
Multiple choice with follow-ups: Here’s where things get really smart—each answer choice gets its own summary, built from the follow-up conversations linked to that path. For example, if students who select “anxious” get a follow-up on what triggers their anxiety, those insights are summarized under the “anxious” node.
NPS (Net Promoter Score): Specific segments student responses by group—detractors, passives, promoters—and gives you tailored summaries for each. That way, if most detractors share similar pain points or needs, you’ll spot it.
If you’re using ChatGPT for this kind of analysis, you can get to the same outcome—it’ll just take more prompting and organization to manually piece summaries by category.
If you’re stuck picking the right survey format, try these best mental health survey questions for high school freshman students or generate your own from a template—no guesswork required.
How to deal with AI context limits when analyzing survey data
Every AI (including ChatGPT or built-in analysis engines) has a context limit: if you have too many survey responses, you simply can’t fit them all in one go. Here’s how I tackle it—both strategies come built-in at Specific:
Filtering: Run AI analysis only on conversations where students answered a specific question, or selected a particular option. This is ideal if you want to dive deep into those who, for example, reported feeling overwhelmed—especially important since nearly one in five adolescents face a mental health disorder [1].
Cropping: Instead of analyzing the entire dataset, send only selected questions to the AI. This focused approach keeps you within technical constraints and gives you sharper, more useful findings on a given topic.
Both methods improve performance and keep your workflow efficient, whether you’re using an advanced platform or sticking to ChatGPT.
Collaborative features for analyzing high school freshman student survey responses
Teamwork on survey analysis is tough. Especially if you’ve got teachers, counselors, and researchers with different priorities—everyone wants to focus on separate mental health issues or student populations.
With Specific, collaboration is built-in. You’re not just chatting with AI alone—you can spin up multiple AI chats, each with their own filters (maybe one for anxiety, one for support systems, another for depression symptoms). Each chat has a clear creator, so you know whose analysis or questions you’re following up on.
See who said what, instantly. Every message shows the sender’s avatar. You can trace back who asked what, compare notes, and avoid stepping over each other. Everyone gets the full context—a huge win for teams working on time-sensitive mental health surveys.
It’s all about workflow efficiency. Want to brainstorm, dig into a specific trend, or hand a thread to someone else? With Specific, it’s smooth, trackable, and way less chaotic than email chains or exported spreadsheets. For more tips on effective survey collaboration, see how to build mental health surveys for students as a team.
Create your high school freshman student survey about mental health now
Start collecting and analyzing meaningful feedback from high school freshman students today—powerful AI analysis, instant insights, and effortless collaboration are just a chat away.