This article will give you tips on how to analyze responses from a college graduate student survey about stipend and financial support using AI-powered techniques and practical workflows.
Choosing the right tools for analysis
The approach and tools you pick for survey analysis depend directly on the form and structure of your data.
Quantitative data: Numbers like “how many students receive stipends?” or average stipend amounts are easy. Just use Excel or Google Sheets—these tools work well for counting and summarizing simple metrics.
Qualitative data: When dealing with open-ended responses or follow-up questions, manual reading gets unmanageable fast. If you want to actually understand every voice in your data set, let’s be honest—you have to use AI tools. These can digest and summarize huge volumes of unstructured feedback, surfacing key themes and perspectives you’d otherwise miss.
When it comes to analyzing extensive qualitative responses, there are two main tooling approaches you should know about:
ChatGPT or similar GPT tool for AI analysis
You can export your survey data, copy it into ChatGPT (or another GPT-based tool), and have a back-and-forth conversation to pull out insights.
It’s flexible: This method gives you control and lets you interrogate the data in creative ways. But as the data set grows, managing it in this way can turn into a headache quickly—you’ll hit copy-paste fatigue, and you may struggle to keep prompts and responses organized across versions.
Not so convenient for big/complex data: If you try analyzing a few dozen or more open-ended answers, it becomes tedious. You’ll likely need to manually chunk the data and keep your own notes on core ideas.
All-in-one tool like Specific
Specific is built for this exact use case: collecting qualitative survey data and instantly analyzing it with AI for actionable insights.
Collects richer data: When you run a college graduate student survey about stipend and financial support in Specific, the AI automatically asks smart follow-up questions on the spot—so you get much deeper answers, not just surface-level feedback. (Learn more on the AI follow-up questions feature page.)
AI-powered analysis: After collecting responses, Specific’s AI instantly summarizes feedback, finds recurring pain points, motivations, and themes—no more spreadsheets or manual coding. You get the big picture, plus the nuance, at a glance.
You can chat with the AI about your results: It works like ChatGPT, but is focused specifically on your data—and you can choose exactly what information is sent to the AI when you chat (great for privacy and focus). You’ll find more details on the AI survey response analysis page.
Useful prompts that you can use to analyze College Graduate Student stipend and financial support surveys
Good prompts are the secret sauce for extracting real insight from your survey data. Here are smart ways to get the most from AI analysis—whether you use ChatGPT or a tool like Specific:
Prompt for core ideas: This is my go-to. It reliably distills the most mentioned themes or concerns from large sets of open-ended responses.
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
Bonus tip: AI gives better answers when you clarify what you’re after. Always give context about your audience (e.g., college graduate students), the stipend and financial support situation, and your research goals. For example:
Analyze open-ended survey responses from U.S. college graduate students about their experiences with stipends and financial support. My main goals are to understand the biggest sources of dissatisfaction and identify improvement opportunities for university policy.
Dive deeper into specific topics: When you see an interesting idea emerge, follow up with something like:
Tell me more about financial anxiety caused by delayed stipends.
Validate specific issues: To check if anyone mentioned a certain pain point, simply prompt:
Did anyone talk about trouble covering rent? Include quotes.
Here are a few other useful prompts for this audience and topic:
Prompt for personas: Ask AI to segment distinct persona types in your survey:
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: Getting an overview of what’s really causing trouble:
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: Understand why students make the choices they do:
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: Get a high-level read on emotional tone:
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.
Want more ideas? Check out this guide on how to create a college graduate student survey about stipend and financial support and see the best questions for this kind of survey.
How Specific analyzes qualitative data by question type
The structure of your survey shapes the way analysis works—whether you use Specific or a manual approach like ChatGPT.
Open-ended questions (with or without follow-ups): Specific provides a summary for all responses, and, if there were follow-ups, you get additional summaries showing what came up in those secondary answers.
Choices with follow-ups: For every option (e.g., “living off-campus” versus “on-campus”), you’ll get a dedicated summary of the follow-up responses linked to each specific choice.
NPS questions: Each NPS category—detractors, passives, promoters—gets its own summary based on why those students rated you as they did. You’ll see what makes promoters happy and what turns detractors off.
You can get similar results in ChatGPT, but it does involve more copy-pasting, organizing, and patience. To see how Specific’s AI chat analysis works in practice, take a look at the AI-powered survey analysis feature.
Overcoming AI context limit challenges
Every AI tool (including ChatGPT and Specific) can only process so much data at once—that’s called the “context limit.” With a large survey, you’ll hit that wall. There are two smart ways to deal with this (and Specific makes both easy):
Filtering: Focus only on conversations where students replied to certain key questions or gave particular answers. You can then send that batch to the AI for streamlined analysis.
Cropping: Select which survey questions should be included in the AI analysis. Leave out questions that aren’t as relevant right now, so you can fit more student voices into each analysis round.
With these tools, you can confidently handle surveys even if you’ve gathered hundreds of rich, nuanced responses from graduate students.
Collaborative features for analyzing college graduate student survey responses
When you’re analyzing survey data about stipend and financial support, collaboration is critical—but it’s tough when feedback is unstructured and spread across multiple files or versions.
Analyze by chatting: Specific lets you analyze survey responses directly by having a chat with the AI—so team members can literally “talk to the data.”
Multiple chats with different angles: You’re not limited to just one thread. You can spin up different AI chats, each focused on their own question (for example, one on difficulties with rent, another tracking how international students experience financial support shortfalls). Each chat has visible filters and shows which colleague created it, so you’re never stepping on someone else’s toes.
Seamless team collaboration: When you and your team are exploring data together, you’ll see avatars showing who asked each analytic question. It’s like a transparent Slack thread for research analysis—each conversation is documented and easy to follow.
This makes sharing findings with administrators or research partners straightforward and brings much-needed transparency to open-ended research. Want a first-hand look at the creation side? Try the AI survey generator to experiment with survey setup and analysis.
Create your college graduate student survey about stipend and financial support now
Start capturing deep, actionable insights from real students—use advanced AI tools, chat-driven surveys, and instant qualitative analysis to make authentic voices impossible to ignore. Create your own survey and see how easy it is to turn student feedback into better support and policies.