This article will give you tips on how to analyze responses from a College Undergraduate Student survey about Diversity and Inclusion. If you’re looking for actionable ways to approach survey analysis with AI, you’ll find solid methods here.
Choosing the right tools for analysis
The tools and methods you use should fit the structure of your survey response data. That decision quickly separates into two paths:
Quantitative data: For clear-cut responses—like "How likely are you to recommend campus events?", or simple single/multiple choice—it's easiest to crunch stats in Excel or Google Sheets. Count, chart, or filter responses and you’ll see patterns jump out in seconds.
Qualitative data: Open-ended answers or AI-powered follow-up responses give the most context, but they’re brutal to analyze manually (especially at scale). You really need to bring in AI tools—no one wants to scroll and code hundreds of comments. Traditional tools just don’t cut it.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-pasting data into ChatGPT works—just export your survey or spreadsheet of text answers and drop it in. You can ask the AI direct questions about your data, extract themes, or summarize pain points. But there are a couple headaches here: handling exports, dealing with context limits (the AI may ignore some responses if you paste too many), and generally, it’s a messy workflow when you want depth—especially for nuanced topics like diversity and inclusion.
Manual process: If you have a small batch, it’s fine. But for any serious survey, this quickly gets clunky and hard to manage.
All-in-one tool like Specific
Purpose-built for survey analysis: With Specific, you get an integrated system: it collects conversational survey data with AI follow-ups and analyzes it in real time—no jumping between platforms. As surveys are filled out, AI probes for clarifying details, improving the richness and reliability of your diversity and inclusion data. Learn how AI follow-ups work.
Instant AI insights: As responses roll in, Specific automatically summarizes them, spots patterns and themes, and gives you actionable summaries. You chat with the AI about the results (just like ChatGPT)—but it also gives you tools to manage what data goes into context, segment responses, and keep everything structured. It removes spreadsheet busywork, so you focus on what actually matters: understanding the campus’s diversity and inclusion story.
User-friendly for any team: The workflow is simple. Start with a conversational survey template for college undergraduates about diversity and inclusion (there’s a ready-made generator for this exact use case), collect high-quality responses with AI-powered follow-ups, and analyze everything from the same dashboard. You can also customize your survey on the fly using their AI survey editor.
Privacy and ease: No copy-paste, secure data storage, and you reduce context-dropping errors common in generic AI tools. If you want a wider range, you can create any custom survey from scratch with their AI survey maker.
This approach is especially valuable as students increasingly expect not just inclusion in surveys, but thoughtful analysis of their voices—a key expectation in higher education research today. Over 70% of higher ed institutions already use at least one AI-powered tool for data analysis, proving demand is high for these smarter workflows. [1]
Useful prompts that you can use for analyzing College Undergraduate Student Diversity and Inclusion survey response data
AI gets you farther, faster—if you ask the right questions. Here are time-tested prompts (all compatible with ChatGPT, Specific, or other GPT-powered systems):
Prompt for core ideas: If you want a concise overview of big-picture topics that surface in your survey, use this:
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—a one-line intro about your survey’s goal, who filled it out, or what you want to understand. Here’s an example:
Analyze the following responses, collected from college undergraduate students, about their experiences and perceptions of diversity and inclusion on campus. My goal: find out what makes students feel included or excluded, and what barriers exist.
Then, use this to dive deeper into anything interesting:
Deeper exploration prompt: "Tell me more about XYZ (core idea)"
Prompt for specific topic: Want to check for something precise? Use this:
"Did anyone talk about microaggressions?" (Tip: add ‘Include quotes’ if you want verbatim responses.)
Prompt for personas: Cluster respondent types by experiences and attitudes towards inclusion:
"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: Surface challenges faced by undergraduates, and spot patterns others might miss:
"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: Understand what motivates students to get involved (or not) in diversity efforts:
"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: Measure the emotional tone of campus climate responses:
"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: Capture actionable recommendations from students:
"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: Spot where the institution may be falling short, according to students:
"Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents."
If you want to get better at survey writing or need survey questions tailored for college students about diversity and inclusion, check out this practical guide on the best survey questions for this audience.
How Specific analyzes qualitative data from different question types
Open-ended questions (with or without follow-ups): Specific gives you a summary covering all responses to the question, along with any related follow-up threads. With every follow-up AI asks, you get even richer, more focused summaries—so those one-line answers turn into layered insight.
Choices with follow-ups: You’ll see summaries not just by overall count, but broken down by each choice with attached open-ends. You know exactly what students who chose "Other" were thinking, instead of losing insight to catch-alls.
NPS questions: Instead of lumping all responses together, each group (detractors, passives, promoters) has its own batch of follow-up summaries. You can analyze what makes for a bad or great experience, side by side.
You can do the same style of segmentation and drilling down in ChatGPT, but it’ll take more manual setup filtering and chunking.
If you want ideas on structuring your survey around these question types, there’s a great step-by-step guide on how to create a college undergraduate survey about diversity and inclusion.
Staying effective when AI context limits get in the way
AI context limits are real: Every AI model can only “see” so many words at once (especially a problem if your survey got hundreds of responses). If you go over that, it might skip or ignore data.
There are two main fixes—built right into Specific:
Filtering: Focus on just the responses that matter to you. Quickly limit analysis to those who answered certain questions or who picked a specific choice—perfect when you want insights around a smaller slice of campus life.
Cropping: Select only the key questions for your analysis, sending only those to the AI. This keeps your data slim and makes sure you fit comfortably inside the AI’s boundaries, maximizing depth for the questions you care most about.
With manual workflows (like using ChatGPT), you’d end up doing a lot more copy-paste work and managing partial analysis runs—which is tricky for making clean presentations to higher-ups or committees looking for real student perspectives. Nearly 80% of higher ed researchers say that context control in AI is now a must-have to avoid losing key respondent voices. [2]
Collaborative features for analyzing College Undergraduate Student survey responses
Analyzing diversity and inclusion survey results often demands collaboration—student affairs, DEI committees, researchers, and faculty may all need to weigh in. Sharing context and findings is key, but it’s tough when working with scattered files or clunky exports.
Chat with AI as a team: In Specific, everyone can analyze and discuss survey insights right in the platform’s AI chat. It’s like having your own research huddle, but with AI speeding up the synthesis.
Multiple parallel chats: You can set up separate AI chats for different viewpoints—maybe one for general themes, another for pain points related to classroom belonging, and another for student group experiences. Each chat can have its own filters applied, so you don’t overwrite anyone else’s insights. You always see which teammate created each chat—critical for sharing ownership of DEI work.
Team visibility and accountability: When collaborating, you see exactly who contributed each insight—each message in the AI chat displays the sender’s avatar. This makes reporting cleaner and group deliberation simpler.
Applying findings to real change: Once your team agrees on patterns, you can quickly turn AI insights into action—whether that’s sharing with administration, designing student support resources, or shaping future surveys. If you need to tweak questions or launch a new iteration, it’s all manageable from one place.
Want to see this in action? Try the NPS survey builder made for college undergraduates on diversity and inclusion right here.
Create your College Undergraduate Student survey about diversity and inclusion now
Quickly launch an AI-powered survey that collects richer answers and gives you actionable insights on campus inclusion. Get smart follow-ups, instant summaries, and collaborative tools made for your entire research team—all in one place.