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How to use AI to analyze responses from student survey about part-time employment support

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Adam Sabla

·

Aug 18, 2025

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This article will give you tips on how to analyze responses from a student survey about part-time employment support using the latest AI-driven techniques and tools.

Choosing the right tools for analyzing student survey responses

The approach and tools you need depend heavily on the type and structure of your survey data. Here’s how I break it down:

  • Quantitative data: If you’re dealing with multiple choice questions or numerical scales (like “How many hours per week do you work?”), basic tools like Excel or Google Sheets work perfectly. You can quickly calculate percentages, averages, and distributions—ideal for tracking things like the increasing number of UK students working during term time, which jumped from 34% in 2021 to 56% in 2024, with an average of 14.5 hours per week [1].

  • Qualitative data: If your survey includes open-ended questions or follow-up answers, things get much trickier. Reading every comment or conversation manually is time-consuming and not scalable—especially with large datasets typical for student feedback. That’s where AI-powered tools shine.

There are two approaches for tooling when dealing with qualitative survey responses:

ChatGPT or similar GPT tool for AI analysis

Using ChatGPT or another GPT tool is the DIY approach. You can copy and paste your exported survey responses into the chat window and prompt the AI to summarize or analyze themes. While this works for small datasets, it’s not very convenient for larger ones. You’ll run into limitations—copying, cleaning, and segmenting data, tracking which response belongs to which question, and managing follow-ups requires lots of hands-on wrangling.

If you want fine control over each conversation or need to experiment with creative prompts, this is workable. But for continuous, robust survey analysis, it’s too clunky for my taste.

All-in-one tool like Specific

Specific is built for this exact problem: collecting, segmenting, and analyzing both quantitative and qualitative survey responses with AI. From the start, it structures data the right way. When a student answers an open-ended question, Specific’s AI will often ask smart follow-up questions to dig deeper, increasing the quality and depth of insights—more on that in our overview of automatic AI followup questions.

The AI-powered response analysis lets you:

  • Instantly see AI-generated summaries for any question or follow-up

  • Spot trends, key motivations, and common pain points across many students

  • Drill into core ideas, compare cohorts, or even chat with the AI about your data—like using ChatGPT, but purpose-built for survey analysis

  • Easily manage, filter, and export insights for your team, no spreadsheets or manual grouping needed

Want to know more? See details on the AI survey response analysis feature.


Useful prompts that you can use for analyzing student survey data about part-time employment support

Once you have your survey responses in hand, prompts are your secret weapon for getting to real, actionable insights. Here are some I use most often:

Prompt for core ideas: This is my go-to for summarizing big-picture themes in a sea of open-ended responses. You can copy this directly into Specific, ChatGPT, or another GPT tool, and it works brilliantly when you have a big dataset with hundreds of students sharing their views:

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 give it more context about your survey, the situation, or your goal. Just add a sentence or two at the start:

This survey was answered by 400 university students in the UK. We asked how they balance work and studies, if they feel supported, and what the key challenges are in their part-time jobs. My goal is to understand what helps or hinders students from combining studies and work.

Prompt for deeper insight: Once you spot a core theme (“insufficient financial support” for instance), try: “Tell me more about what students said regarding financial support or student loans.”

Prompt for specific topic: When you want to validate or disprove a hypothesis (e.g., “Do students want more flexible work options?”), use: “Did anyone talk about flexible work options? Include quotes.”

Prompt for personas: I like this one for building empathy. Ask: “Based on the survey responses, identify and describe a list of distinct student personas—summarize key characteristics, motivations, and relevant quotes.”

Prompt for pain points and challenges: Get an AI-generated list of the biggest obstacles: “Analyze the responses and list the most common pain points, frustrations, or challenges mentioned. Note patterns and how often they occur.”

Prompt for motivations & drivers: Discover what’s pushing students into part-time work with: “From the survey conversations, extract the primary motivations, desires, or reasons students give for working alongside their studies. Include evidence.”

Prompt for sentiment analysis: To gauge the emotional tone: “Assess the overall sentiment in the survey—positive, negative, neutral. Identify quotes that best capture each sentiment.”

With a handful of prompts like these (and a structured dataset), you can peel back the layers and see what really matters to students. You’ll find more inspiration for writing better survey questions in this guide on best questions for a student survey about part-time employment support.

How Specific handles analysis by question type

Depending on how you structured your survey, Specific tailors the analysis workflow for you:

  • Open-ended questions, with or without follow-ups: The platform creates a summary covering all responses to each question, and includes any insights from automatic or manual follow-up questions. This is where students’ nuanced feedback—like frustration over government loan inadequacies, which nearly 60% reported as not covering basic living costs [2]—really stands out.

  • Choices with follow-ups: When a student selects a specific answer and provides an explanation, their feedback is summarized separately per choice. So if you want to know what students who work >15 hours per week say about their challenges, it’s a click away.

  • NPS (Net Promoter Score): Each segment (detractors, passives, promoters) gets its own summary of all the respective feedback, with follow-up answers gathered and synthesized by the AI for maximum insight.

You can do all this in ChatGPT (copy-paste, organize, prompt), but honestly, it’s a heavier manual lift. For those who value speed and structure, Specific gives you an immediate advantage. If you're looking to create a purpose-built survey from scratch, try the student survey generator for part-time employment support.

Solving the challenge of AI context limits in survey analysis

Even the best AI has context size limits—there’s only so much data it can process at once. If your survey gets hundreds or thousands of student responses, it might not all fit in one analysis run. Here’s how I get around this in Specific (and you can adapt these tricks to DIY projects, too):

  • Filtering: Before running analysis, filter the dataset to only include conversations where students replied to a particular question, or chose a certain answer (“Only students who said their loan does not cover living costs”). This keeps the dataset sharp and focused.

  • Cropping: Select just the set of questions you want analyzed—skip demographic or filler questions, and focus AI on the critical feedback areas. This not only keeps you within context limits, but often uncovers more concrete insights.

Both of these strategies are immediately available in Specific’s AI response analysis tool—just a couple clicks versus lots of filtering and reformatting if working by hand.

Collaborative features for analyzing student survey responses

Survey analysis shouldn’t be a one-person show. When working on student surveys about part-time employment support, it’s common for program managers, researchers, and career advisors to all want input on the findings—and this is where real collaboration can stall.

Analyze data by chatting: In Specific, I can chat directly with the AI about findings while my colleagues launch parallel chats analyzing the same (or filtered) data from another angle. It’s like running multiple interactive brainstorming sessions on your raw survey results.

Multiple chats for perspective: Each analysis chat can have its own filter or focus—one for financial support, another for work-life balance, etc. Each chat clearly shows who created it, tying an analysis back to its author. This helps avoid overlap, confusion, and steering the team in different directions.

Clear attribution and transparency: In collaborative AI chats, you’ll always know who said what—every message is attributed using sender avatars. This makes it a breeze to keep track when collaborating with colleagues, or sharing findings with a wider team for review.

These collaborative workflows make it easier to turn student feedback into real support programs—faster, and with less friction. For tips on survey design, check out how to easily create a student part-time employment support survey.

Create your student survey about part-time employment support now

Get real insights fast—create a student survey about part-time employment support using AI, analyze data collaboratively, and surface what students need most today with Specific’s advanced conversational survey tools.

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Sources

  1. Financial Times. University students in UK work more as grants fall short

  2. Financial Times. UK students struggle with finances as loans fail to cover living costs

  3. Financial Times. Open University research: student working hours and academic impact

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.