This article will give you tips on how to analyze responses from a college graduate student survey about mental health and well-being using AI for faster and deeper insights.
Choosing the right tools to analyze your survey responses
Choosing an approach (and tool) for survey analysis depends on the structure and type of data you collect from your college graduate student mental health and well-being survey.
Quantitative data: These are things like how many students selected each choice, or their NPS score. They’re straightforward—just use Excel, Google Sheets, or built-in dashboard reports from your survey platform to crunch the numbers and visualize trends.
Qualitative data: Open-ended responses and in-depth follow-up replies give you rich context, but they’re tough to manually sift through—especially at scale. Reading every response isn’t practical. That’s where AI tools come in, helping you process and summarize large volumes of unstructured feedback efficiently.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
You can copy your exported data into ChatGPT (or any major GPT-based tool) and interactively ask questions about your responses.
While this approach works, it’s not optimized for survey analysis. The copy-paste workflow quickly becomes clunky as your dataset grows, and you’ll hit context window limits, making it hard to analyze everything at once.
All-in-one tool like Specific
Specific is purpose-built for survey analysis using AI. You can collect data with smart, conversational surveys that probe for details—thanks to automated follow-up questions (see how automatic AI follow-ups work). This boosts the quality and clarity of every response you capture.
On the analysis side, Specific does the heavy lifting for you. Its AI instantly summarizes responses, finds key themes, and surfaces actionable insights—no more manual data prep or switching between tools. You can chat directly with the AI about your data, just like you would in ChatGPT, but with extra features that let you filter, segment, and manage what gets sent to the AI context. Get a full breakdown of what’s possible with AI survey response analysis in Specific.
Useful prompts that you can use for analyzing college graduate student survey responses about mental health and well-being
When you’re analyzing open-ended responses, the quality of your prompts directly shapes the value of your output. These AI prompts will help you get to the heart of what your college graduate students are really saying about mental health and well-being.
Prompt for core ideas: This is the workhorse prompt used by experts—in fact, Specific uses it behind the scenes. Try it in any GPT 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
Give AI more context for better results. Describe your audience, goals, or even share what you’re hoping to learn:
Analyze these survey responses from recent college graduates about challenges they’re facing with mental health and well-being. Highlight recurring themes and patterns, and note any topics related to adjusting to post-graduation life.
Prompt for follow-up on a theme: If you want to dig into a specific idea discovered in the summary, ask: "Tell me more about XYZ (core idea)"
Prompt for specific topic mention: To verify if something came up (or was missing):
"Did anyone talk about burnout?"
Pro tip: Add “Include quotes” to see direct feedback.
Prompt for personas: Identify distinct groups in your audience. “Based on the survey responses, identify and describe a list of distinct personas—similar to how ‘personas’ are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.”
Prompt for pain points and challenges: “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 motivations & drivers: “From the survey conversations, extract the primary motivations, desires, or reasons participants express for their behaviors or choices. Group similar motivations together and provide supporting evidence from the data.”
Prompt for sentiment analysis: “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 & ideas: “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 & opportunities: “Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.”
How Specific analyzes different types of survey questions
Open-ended questions (with or without follow-ups): Specific gives you a concise summary for all base responses and the related follow-ups, so you understand the full context behind each answer. This approach mirrors best practices recommended by mental health research experts like Laurie Santos, who advocates for understanding the nuances behind student stress and well-being. [4]
Choices with follow-ups: Each option (for example, “Struggling with anxiety”) gets a separate collection of summarized follow-up responses. You can see, at a glance, which issues produce the most comments or concerns.
NPS (Net Promoter Score): For each group—detractors, passives, promoters—Specific provides a distinct summary of the associated feedback. This makes it easy to spot what’s working for your most satisfied respondents and where you’re losing engagement with others.
You can certainly replicate this using ChatGPT, but it takes more manual steps and careful data slicing to get comparable, organized results.
What to do when your data set is too large for AI’s context window
AI tools like GPT have context size limits—if you load in hundreds of graduate student survey responses, the AI might not be able to handle them all at once. This becomes a real roadblock, especially as mental health surveys often generate large volumes of open feedback (a trend that’s accelerated following the surge in demand for campus mental health services [2]).
You have two simple ways to work around this (both are handled automatically in Specific):
Filtering: Only analyze conversations where students replied to specific questions or selected certain options. This way, you can zoom in on a segment—like those who specifically mentioned burnout or loneliness.
Cropping: Limit which questions are included when sending data to the AI. For example, just look at answers to the “What challenges are you facing?” section, ignoring less relevant questions to save space.
Both strategies help you efficiently analyze more data, regardless of your tool.
Collaborative features for analyzing college graduate student survey responses
Collaborating on analysis can be painful—whether you’re working with faculty, mental health counselors, or your own team. Aligning everyone on key insights about college graduate student mental health and well-being is rarely straightforward.
Analyze together by chatting with AI: In Specific, you can spin up multiple analysis chats on the same data set. Each “thread” can have its own angle—maybe one for student stress, one for support needs, and another for tracking post-grad adjustments.
Parallel analysis with filters and attribution: Every analysis chat can have different filters applied (like looking only at responses from students who mention anxiety). You always see who created which chat, making teamwork and responsibility clear across groups or committees.
Transparency in collaboration: With visible avatars beside each chat message, it’s simple to trace who made which interpretation or comment. This helps keep everyone on the same page, builds consensus, and fosters higher-quality insight from different perspectives. If you want a full guide on structuring your college graduate mental health survey for better team collaboration, check out this practical how-to article.
Explore and compare insights efficiently: Since responses (especially to mental health questions) often touch on sensitive, nuanced issues, being able to easily compare notes with your team in one place is a huge boost—not just for researcher efficiency but also for ethical interpretation.
Create your college graduate student survey about mental health and well-being now
Get richer insights, automate your analysis, and empower your whole team to explore what really matters to college graduates by creating an AI-powered conversational survey tailored for mental health and well-being.