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How to use AI to analyze responses from college doctoral student survey about funding and stipend adequacy

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Adam Sabla

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Aug 29, 2025

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This article will give you tips on how to analyze responses from a College Doctoral Student survey about Funding And Stipend Adequacy using AI and smart tooling for survey response analysis.

Choosing the right tools for analyzing College Doctoral Student survey responses

The best approach and tools for analyzing survey data depend on the format and structure of your responses:

  • Quantitative data: If you’re looking at how many students selected a specific funding source or rated stipend satisfaction as “adequate,” tools like Excel or Google Sheets get the job done fast. You’ll just tally answers and do some simple math.

  • Qualitative data: Things get trickier when you collect open-ended answers or follow-ups—like students describing stipend challenges or sharing ideas for improvement. Reading every reply yourself isn’t scalable, especially when you want to surface patterns and insights from dozens or hundreds of stories. This is where AI steps in as your research sidekick.

When working with free text answers or multi-turn conversations, two main AI tooling approaches stand out:

ChatGPT or similar GPT tool for AI analysis

Copy-pasting raw data into ChatGPT does work—you can export responses from your survey, drop them into a GPT chat, and ask questions like, “What are common themes?” or “Who talked about financial stress?”

It’s quick, but gets messy fast. OpenAI’s context limits mean you’ll sometimes need to split data up or figure out which responses to ignore. You’ll also manage follow-up and filters manually, and repeating analyses with new data isn’t seamless.
Still, if you’re running a one-off analysis on a smaller survey, this approach can provide a real productivity boost over manual review.

All-in-one tool like Specific

Specific is purpose-built for the whole workflow, from collecting conversational survey data to AI-powered analysis. When you design your survey for College Doctoral Students about funding and use Specific, you can leverage:

  • Conversational data collection: Respondents chat in natural language, with AI follow-up questions probing for more detail as needed—automatically. This boosts data quality and surfaces richer context. Read about AI-powered follow-up questions for specifics.

  • Instant AI analysis: With a single click, Specific summarizes all open-text responses, finds recurring themes (such as funding gaps or stipend complaints), and organizes insights, eliminating manual review and spreadsheets. Explore AI survey response analysis for a demo.

  • Conversational reporting: Just like with ChatGPT, you can chat with the AI to explore the data in depth, but with additional controls for data filtering and question cropping—purpose-built for survey analysis.

This workflow saves hours and produces more robust, actionable findings. If you’re often running similar AI surveys—or need teamwork features—this is the approach I recommend.

Useful prompts that you can use for College Doctoral Student Funding And Stipend Adequacy survey data

Clear, well-structured prompts unlock better AI analysis, whether you use ChatGPT or a specialized tool like Specific. Here are proven prompts I rely on—and context tips to avoid generic summaries:

Find core ideas and themes: Use this generic “core ideas” prompt for getting crisp topics and a numbers-based summary. Specific actually uses a version of this under the hood—it’s excellent for any large dataset.

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

I’ve found AI analysis improves a lot when you add 1-2 sentences of context up front about the survey’s purpose, who answered, and your research goals. For instance:

This is a survey of college doctoral students about their funding and stipend adequacy. Please focus your analysis on barriers to financial stability, debt burdens, and personal experiences with university funding programs.

Drill down on any insight: Once a theme pops up—say, “high living costs” or “debt”—ask the AI: “Tell me more about XYZ (core idea).” It can summarize sub-themes, show representative quotes, or group responses.

Spot-check for a specific topic: When you want to confirm if anyone mentioned a point, or search for outlier cases, use:

Did anyone talk about [XYZ]? Include quotes.

Uncover common pain points and challenges: Great to understand obstacles around funding and stipends.

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.

Persona extraction: Ideal if you want to segment doctoral student experiences—for example, by field of study, gender, or financial background.

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.

Sentiment analysis: Quickly gauge overall mood—are most students frustrated, neutral, or optimistic about stipends?

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.

These prompts work well with both AI survey tools like Specific and direct GPT chat analysis.

How Specific summarizes qualitative data for each question type

When you analyze survey data with Specific, the software automatically tailors its summaries based on the question type:

  • Open-ended questions (with/without follow-ups): You’ll see a clear summary for all primary responses, followed by more detailed summaries for each follow-up topic or clarifying question. This is gold for surfacing financial anxiety or creative coping strategies students share.

  • Choices with follow-ups: Each option (such as “funded by university” or “self-funded”) has its own condensed summary of follow-up replies, helping you understand not only what’s chosen, but why.

  • NPS questions: For Net Promoter Score surveys—like “How likely are you to recommend your doctoral program to others?”—Specific buckets all follow-up explanations by category: detractors, passives, and promoters. Each group gets a focused narrative summary, so you instantly see what drives loyalty or discontent.

You can replicate this process in ChatGPT, but you’ll need to separate and tag data yourself for each analysis pass.

Solving context limit challenges with AIs on large surveys

Every AI—from GPT-4 to Claude—has a context (input) size limit. When your College Doctoral Student survey on Funding And Stipend Adequacy collects dozens or hundreds of in-depth responses, you’ll eventually hit that wall. Here’s how I get around it (and what Specific has built in):

  • Filtering: Only send conversations where students answered specific questions or chose certain responses to the AI. For instance, analyze just those who discussed debt or responded to funding challenges, not those who skipped them entirely.

  • Cropping questions: Select only the survey questions of interest to include in your AI analysis. This shrinks the data size, so you can dive deep on, say, “Describe your living expenses,” without running into the token limit.

Both approaches keep you within the context boundaries, letting the AI do meaningful work on as much data as possible.

Collaborative features for analyzing College Doctoral Student survey responses

Team-based analysis of funding and stipend surveys can turn into a mess of scattered spreadsheets, endless comment threads, and versioning headaches. I’ve seen this first-hand, and it kills both clarity and momentum.

Direct AI chat for survey data: With Specific, you analyze all responses just by chatting with the AI—it’s like having a group Slack thread, but about the survey’s actual findings.

Multiple chats, each with custom filters: You can spin up as many analysis chats as needed. Each chat can focus on a different research question—like gender disparity in funding, debt burden by department, or stipend satisfaction trends. Filters are easy to apply, and every chat shows who started it.

Team visibility & accountability: As colleagues join or contribute to analysis, their avatars show next to messages. It’s easy to see who raised which idea or asked which follow-up. This makes it a breeze for faculty, student councils, or institutional researchers to work together without duplicating effort or missing critical perspectives.

For more on building surveys your team will love analyzing together, check out our walkthrough on creating college doctoral student funding surveys and explore the AI-powered survey editor for a glimpse of how simple it is to adapt questions for your own use.

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Sources

  1. researchdeep.com. How Much is a PhD Stipend?

  2. talentsearchbgw.com. Living Costs in U.S. Cities: Graduate Student Budgeting

  3. psypost.org. Doctoral psychology students have not kept pace with cost of living

  4. wiareport.com. Large gender disparities in doctoral education funding

  5. forwardpathway.us. Boston Colleges PhD Stipend Increase

Adam Sabla - Image Avatar

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.

Adam Sabla

Adam Sabla is an entrepreneur with experience building startups that serve over 1M customers, including Disney, Netflix, and BBC, with a strong passion for automation.