This article will give you tips on how to analyze responses from a college undergraduate student survey about mental health and well-being using AI-driven approaches for survey response analysis, so you can move faster from data to real insights.
Choosing the right tools for survey analysis
The approach and tooling you’ll need depend entirely on the form and structure of your survey responses—each type needs a different touch.
Quantitative data: If you’re handling data such as “How many students felt overwhelmed last month?” these are easy to count and summarize in tools like Excel or Google Sheets. You’ll spot basic patterns by charting or running pivot tables.
Qualitative data: If you have open-ended questions (“Describe your mental health challenges”) or detailed follow-ups, it’s a different beast. Reading each response yourself isn’t practical when sample sizes grow—which is exactly when you want insights most. This is where powerful **AI tools** step in: they can read hundreds of conversations, spot themes, and summarize nuanced feedback for you.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste-friendly, but limited by workflow. You can export your survey data—say, from Google Forms or your survey tool—and paste it into ChatGPT or similar platforms. Then, you prompt it to find patterns, summarize key takeaways, or answer specific follow-up questions.
Handy for quick queries, but clunky for lots of data. When your survey grows—maybe dozens or hundreds of students wrote multi-paragraph answers—copy-pasting gets messy. You’ll have to chunk the data, repeat prompts, manage context limits, and keep track of what's been analyzed already. There is also a risk of losing connection between follow-ups and their main responses.
All-in-one tool like Specific
Purpose-built for survey collection and analysis. Specific is designed for this use case: collect survey data via conversational (AI-driven) surveys, including real-time follow-up questions that make the data richer and more contextual (learn about automated AI follow-ups).
AI-powered, structured analysis from the start. Instead of overwhelming spreadsheets, you get instant AI summaries. The platform distills insights from all responses (including open-ended answers and follow-ups), surfaces key themes, and groups supporting quotes for easy reporting.
Conversational analysis experience. You chat with the results, just like in ChatGPT, but with extra features: you can filter by question, segment conversations, and stay within contextual limits more easily. See all the details at AI survey response analysis.
No export or manual wrangling needed. The analysis is ready right where your survey data lives—saving you time and keeping everything in context.
Useful prompts that you can use for analyzing College Undergraduate Student mental health and well-being survey data
Once you’ve got your survey responses, the right prompts unlock actionable insights no matter what tool you use. If you’re using ChatGPT, or even built-in analysis in platforms like Specific, these work well:
Prompt for core ideas: This is great for surfacing central themes from a large batch of responses. I recommend this as a starting point:
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 when you provide more context about your survey, your situation, and your goals. For example, instead of just dumping the data, give a one-line brief first:
“This is survey data from undergraduate students about mental health and well-being during the 2023-2024 academic year. Most respondents were first- or second-year students at public universities in the US. I want to understand the main issues and what suggestions are most common.”
Prompt to dig deeper into themes: Once you find an idea or pattern (“academic stress” comes up a lot), ask the AI to expand:
Tell me more about academic stress (core idea)
Prompt for specific insights: If you want to check if a particular topic was discussed, try:
Did anyone talk about counseling services? Include quotes.
Prompt for personas: To understand groups among your students, try:
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: For listing top issues mentioned:
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: To break down the mood overall:
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 unmet needs and opportunities: This is crucial for planning interventions or policy changes:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
For a deeper dive into creating questions and framing your survey for high-quality, analyzable data, check out these resources on best questions for college undergraduate student mental health and well-being surveys and tips to create a survey for college students.
How Specific analyzes qualitative survey data by question type
Specific is built to automatically make sense of the different structures your survey might have. Here’s how:
Open-ended questions with or without follow-ups: You get a complete summary for all responses, including insights from dynamic follow-up questions—so you see both initial answers and deeper context.
Choices with follow-ups: When you have a multiple-choice (e.g., “What is your main stressor?”) and students provide extra feedback, Specific summarizes responses for each choice—so you can compare why students picked academics vs. finances, for example.
NPS questions: Net Promoter Score analysis is broken down by promoters, passives, and detractors; each group’s follow-up responses are summarized separately, making it simple to pinpoint what drives loyalty vs. dissatisfaction.
You could do the same analysis with a tool like ChatGPT, but it would require more manual sorting, filtering, and re-prompting for each question and response category.
Want to create an NPS survey for this audience? Try Specific’s NPS survey for college undergraduate students about mental health and well-being or the full-featured AI survey generator for college undergraduates and mental health.
Tackling challenges with AI context limits for large response sets
One practical issue when using AI tools like GPT is context size limits: there’s only so much text you can send to the model at once. If your survey receives lots of responses, you risk running into that ceiling and missing insights.
There are two main ways to keep analysis manageable, both offered by Specific out of the box:
Filtering: Only send a subset of conversations to the AI for analysis—for example, just those students who discussed “stress” or who scored low on well-being. This narrows your data to what matters most.
Cropping questions: Select only the most important questions or answer threads for the AI to review. This way, you avoid blowing past context limits and keep the analysis fast and focused.
Both strategies ensure you get depth and breadth, even on large, open-ended data sets. If you’re analyzing manually via ChatGPT, you’ll need to replicate this filtering and cropping workflow yourself.
If you want guidance from the start, building your survey in a way that’s easy to analyze, I recommend using the AI survey generator or AI survey editor.
Collaborative features for analyzing college undergraduate student survey responses
Collaborating on survey analysis for college mental health and well-being can get messy fast—especially when multiple researchers, staff, or student advocates want to dig into the data, draw conclusions, and recommend changes.
Easy collaborative AI analysis. In Specific, you chat with AI about your survey responses, and anyone on your team can join in. No need to send spreadsheets or copy-paste quotes into email threads.
Multiple chats, multiple perspectives. Each member can spin up separate analysis “chats,” each filtered for their unique focus—a chat digging into anxiety triggers, another just for financial stress, someone else targeting help-seeking behaviors. You always see who started each thread and which filters apply.
Clarity of communication. As your team chats in Specific’s analysis interface, each message shows who wrote it—including avatars for clear accountability and smoother collaboration. It’s perfect for splitting deep-dive tasks or consensus-building across student services, counseling centers, and administration.
This dynamic workflow is especially useful as you address the serious reality in today’s student population: for example, 76% of college students experienced moderate to serious psychological distress in 2023, and more than 8 in 10 who face academic challenges say it causes substantial distress [1][2]. Being able to pull the right insight, quickly and collaboratively, often makes the difference between good intentions and meaningful action.
Create your college undergraduate student survey about mental health and well-being now
Start collecting rich, actionable feedback and let AI do the heavy lifting—capture nuanced insights, collaborate with your team, and improve student well-being with data that drives results. Create your own conversational survey and experience the power of fast, accurate response analysis today.