This article will give you tips on how to analyze responses from a student survey about peer mentoring. Whether you’re looking for core insights or patterns, I’ll show you clear ways to use AI and the best prompts to uncover what really matters.
Choosing the right tools for survey response analysis
The right approach starts by understanding the data from your student peer mentoring survey. What you need to use depends on whether you have quantitative or qualitative responses:
Quantitative data: Numbers, counts, and ratings—like the percentage of students who felt peer mentoring helped them—are easy to work with in Excel or Google Sheets. You can quickly tally up responses and look for trends.
Qualitative data: Open-ended responses or answers to follow-up questions are where things get tricky. Reading dozens or hundreds of detailed responses by hand isn’t realistic. Here you need an AI tool: something that can process text, extract core ideas, and summarize what your students are telling you.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Manual GPT-powered analysis: You can copy your exported survey data and paste it into ChatGPT or a similar tool. This lets you chat with the AI and ask questions like, “What are the main themes in these peer mentoring feedback responses?”
Limitations: It works for small sets of data, but becomes clunky for larger surveys. Organizing, filtering, and keeping track of context is mostly manual. As your responses grow, it’s easy to lose track of which questions feedback relates to, and adapting your prompts to get comprehensive results takes extra effort.
All-in-one tool like Specific
Purpose-built for surveys: Tools like Specific are designed exactly for this use case. They combine AI-powered collection (the survey asks smart follow-up questions) with built-in analysis features that summarize, sort, and let you interact with data effortlessly.
Higher quality responses: Because surveys can ask tailored, real-time follow-ups, the feedback you collect is richer—students’ perspectives on peer mentoring are explored in more depth than from a static form. Automatic AI follow-up questions ensure you don’t miss context.
Instant actionable insights: Analysis is handled automatically. The AI summarizes all responses, highlights top ideas, and even lets you chat about your survey results (think ChatGPT, but context-aware and designed for surveys). Features for filtering, organizing, and managing what’s sent to AI make it far less labor-intensive than generic tools.
If you prefer to create a survey tailored for your student audience about peer mentoring, Specific’s survey generator can help from the start.
Useful prompts that you can use for student survey about peer mentoring
Getting quality insights from your peer mentoring survey depends on asking your AI the right questions. Here are prompts that work well for analyzing student feedback:
Prompt for core ideas:
This is the go-to for quickly surfacing central themes. Paste your data (or segment of it) and use the following:
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
Tip: AI always delivers better results with more context. For example, if your goal is to find out how peer mentoring supports first-year students at your university, tell the AI:
This survey was conducted among undergraduate students who participated in peer mentoring. We want to better understand how peer mentoring impacted their academic performance and overall integration within the university community.
Prompt for further details: If you spot a theme, go deeper with: “Tell me more about [core idea]”
Prompt for specific topic: Looking for targeted feedback? Try:
Did anyone talk about mentee-mentor relationship quality? Include quotes.
Prompt for personas: Great for identifying groups with distinct experiences:
Based on survey responses, identify and describe a list of distinct personas… 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: Useful if you want to know what students struggled with:
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: Find out what motivates participation, e.g.:
From the survey conversations, extract the primary motivations, desires, or reasons participants express for their involvement in peer mentoring. Group similar motivations together and provide supporting evidence from the data.
Prompt for unmet needs & opportunities: Want to improve your program? Ask:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by students.
For a deep dive on creating or refining questions for your survey, check out best questions for student survey about peer mentoring.
How Specific analyzes different types of survey questions
Open-ended questions (with or without follow-ups): Specific analyzes every answer, plus all related follow-up responses. This means you get a comprehensive summary of what students shared about, say, feeling welcome in the mentoring program. The AI ties the context together so your results aren’t just isolated snippets—they form a complete picture.
Choices with follow-ups: For multiple-choice questions with follow-up prompts ("Why did you choose this option?"), Specific aggregates and summarizes each choice’s follow-up feedback separately. This helps you see, for example, why students chose “strongly agree” versus “neutral” on program satisfaction.
NPS (Net Promoter Score): Specific automatically categorizes responses from detractors, passives, and promoters, then gives you a summary of open-text follow-ups for each. You see exactly what motivates high scores and what’s holding back lower ones—for example, common pain points or standout benefits.
You can handle these types of breakdowns in ChatGPT, but it usually means a lot more manual data structuring and prompt-making. Specific does the sorting for you, as the results are automatically tied back to your survey’s question flow.
If NPS is your main metric, you might want to try the NPS survey generator for students.
How to work around AI’s context size limits in survey analysis
AI tools only process a limited amount of text at once—too many survey responses, and you’ll hit a wall. Here’s how to handle it (both approaches are built into Specific, but you can also adapt these strategies manually):
Filtering: Only include conversations where students answered certain questions or picked specific choices that you want to analyze. This puts you in control over what’s processed by the AI and keeps the data set focused.
Cropping: Select only the questions (and related replies) most relevant to your analysis. That way, the AI spends its “attention” on what matters most, instead of running out of space on less important conversation threads.
For large student data sets, this means you can still get nuanced insights without overloading your AI tool. Learn more about how Specific’s AI survey response analysis manages this out of the box.
Collaborative features for analyzing student survey responses
Collaborating on analysis can get messy fast—especially for peer mentoring surveys where multiple team members need to weigh in. From educators to program designers, everyone sees the data through a different lens.
Chat-based collaboration: In Specific, the AI chat feature means you can analyze survey responses just by chatting—with the AI and with your teammates. Share insights, ask new questions, and see fresh perspectives right in the chat. Multiple chats can run in parallel, each with its own filters and focus. You always see who started each conversation, so collaboration stays organized and transparent.
Attribution and context: Every message in the collaborative chat shows who said what via avatars. This little detail makes it easier to bring others into the analysis, get alignment, and share updates on what you’re learning from the survey results.
Effortless segmentation: Each analysis chat can be filtered by role, cohort, or question type, letting you compare, for example, feedback from first-year mentees versus upper-year mentors. No spreadsheet juggling—just conversational, team-based learning.
Want to make the survey creation and analysis even easier? The AI survey editor lets you update question structure, follow-up logic, and tone via natural language—so you can keep refining your survey on the fly.
Create your student survey about peer mentoring now
Start analyzing what matters—instantly summarize responses, find hidden patterns, and unlock actionable insights with AI-powered tools made for real student feedback. New insights are just a survey away.