This article will give you tips on how to analyze responses from a College Graduate Student survey about Diversity and Inclusion using the best AI and manual techniques.
Choosing the right tools for survey analysis
Your approach depends a lot on the kind of data you get from your survey, and the tools should match the structure of those responses.
Quantitative data: For anything that’s easy to count (how many students selected an option, or rated something on a scale), you can tackle the analysis with classic tools like Excel or Google Sheets. These handle stats, charts, and rankings with ease.
Qualitative data: When you’re dealing with open-ended answers or follow-up comments, the volume and messiness mean you can’t just read everything. Here, AI tools become essential—they turn big piles of text into summaries, themes, and insights you can act on.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Copy-paste and chat: The simplest approach is to export your responses (usually as CSV) and paste big chunks into ChatGPT. You can then ask questions or summarize what students said—very similar to chatting with a smart assistant.
Downsides: This works, but it has limits. You’ll run into copy-paste hassles, context size barriers, and it’s tough to manage or track conversations when analysis gets deep or you want to revisit your findings.
All-in-one tool like Specific
Purpose-built for survey analysis: Specific was designed from the ground up for this. It handles everything: creating the survey, asking smart follow-up questions to get richer responses, and analyzing the answers with AI.
Deeper insights with better data collection: When you use automatic AI follow-ups, you get richer, clearer responses from students. That means more reliable insights when you analyze later.
One-click AI summaries and instant chat with your data: With Specific’s AI-powered analysis you get a summary of all responses, see core themes, and can chat with the AI to ask anything about your data. You don’t need to jump between tools or copy-paste endlessly. You also get full control over what data is sent to the AI for analysis.
For more on survey creation, see College Graduate Student survey generator for diversity and inclusion and AI survey generator from scratch.
Other platforms like NVivo and MAXQDA provide similar AI-driven features for qualitative data—using tools like automated coding and sentiment analysis—which can give a helpful overview, but tend to require more manual setup and lack the "chat with your results" experience that Specific provides. [3]
Useful prompts that you can use for College Graduate Student diversity and inclusion survey analysis
AI-powered tools are only as good as the prompts you use. Here’s how to get better answers from your data, whether you use Specific, ChatGPT, or another tool.
Prompt for core ideas: If you want a clean, bullet-pointed summary of what students are actually talking about, use this core idea prompt. It’s tried and tested—Specific relies on it for its analyses. Paste it straight into your AI chat or use automatically in Specific:
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 more context for better answers: The more you tell the AI about your survey and your goals, the better your analysis will be. Instead of just asking “What did people say?” try something like:
This survey was completed by college graduate students in 2024. The main goal is to understand their experiences and concerns around diversity and inclusion in higher education. Summarize the key topics that students mention around this.
Prompt to dig deeper: Once you have a list of core ideas, zoom in by asking, “Tell me more about XYZ (core idea).” This lets the AI focus in on hot spots or new themes.
Prompt for specific topic or validation: To make sure you didn’t miss something, ask “Did anyone talk about [e.g., campus climate, pay equity, faculty diversity]? Include quotes.” This surfaces supporting evidence or nuanced comments.
Prompt for personas: If you want a better sense of who’s saying what, use: "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: Highly relevant for this topic: "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 see what mood or attitudes dominate: "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: Helpful for actionable recommendations: "Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant."
See guide on best questions to ask in diversity and inclusion surveys for graduate students for inspiration before running your analysis.
How Specific analyzes qualitative data by question type
Specific structures the analysis so you always get summaries tailored to the type of each survey question:
Open-ended questions with or without followups: You get a single, clear summary that covers both the main question and the follow-ups, so you can see the big picture and deeper explanations in one place.
Choice (multiple-choice) questions with followups: Every answer choice comes with its own AI-generated summary for all follow-up responses related to that choice. That means you can understand not just what students picked, but why—their reasoning, feelings, and unique concerns.
NPS (Net Promoter Score): Each category (detractors, passives, promoters) gets an individual summary of all the follow-up comments tied to it. If five passives mention campus climate or three detractors talk about pay inequity, you see that pattern immediately.
You can do the same thing by formatting your data and using ChatGPT, but it takes a lot more manual labor—especially sorting by question type and keeping the summaries organized.
Related: How AI-generated follow-ups work inside Specific.
How to tackle AI context size limits in survey analysis
AI models—whether in Specific, ChatGPT, or other tools—can’t handle unlimited amounts of text at once. When you have hundreds of survey responses, you’ll hit this “context limit.” Here’s how to work around it and keep your analysis effective:
Filtering: Only include survey conversations where students replied to particular questions or gave specific answers. This means only the most relevant data is analyzed, freeing up valuable space in the AI’s “attention span.”
Cropping: Select only the questions that matter most for your analysis. You can exclude off-topic or filler questions, ensuring the AI focuses on what’s crucial—and your most valuable data fits in the available context window.
In Specific’s AI chat analysis, both of these approaches are built in and super simple to set up.
Collaborative features for analyzing College Graduate Student survey responses
One of the toughest parts of analyzing college graduate student diversity and inclusion surveys is making sure everyone can review, discuss, and contribute to the findings—without losing track or duplicating work.
Instant AI chat analysis, together with your team: With Specific, everyone can dive in and analyze data just by chatting with the AI—no intimidating dashboards or technical wrangling required.
Multiple analysis threads and ownership: You can start multiple chats on the same data, each filtered for a different slice (e.g., campus inclusion, faculty diversity, pay disparities). Every chat shows who created it, so your team can split up work or compare conclusions in parallel.
Clear visibility and accountability: In AI Chat, each message now shows the sender’s avatar. You’ll always know who asked what, which matters for larger research teams or when sharing findings with stakeholders.
For more, check the easy guide to building and analyzing college graduate student diversity surveys.
Create your College Graduate Student survey about diversity and inclusion now
Jump straight into rich, actionable insights about diversity and inclusion among graduate students—combine deeper follow-up conversations with instant AI-powered analysis, all in one place, no manual labor required.