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How to use AI to analyze responses from online course student survey about likelihood to recommend

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Adam Sabla

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Aug 21, 2025

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This article will give you tips on how to analyze responses from an online course student survey about likelihood to recommend. If you're looking to turn survey data into actionable insights, you're in the right place.

Choose the right tool for analyzing survey responses

When you dig into the responses from online course student surveys, the right approach and tools depend on how the data looks.

  • Quantitative data: If you’re looking at things like the number of students who rated a course highly or selected a certain answer, you can tally and analyze this in tools like Excel or Google Sheets. These platforms are great for straightforward counts, averages, and quick charts.

  • Qualitative data: If your data comes from open-ended or follow-up questions—those detailed, story-rich answers—there’s simply too much to read and organize by hand. For deep dives, you’ll get the most value from AI-powered analysis tools that spot patterns and themes automatically. This is where manual approaches start to hit their limit, and automation is essential.

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

ChatGPT or similar GPT tool for AI analysis

You can copy and paste survey responses into ChatGPT or another large language model. Then simply ask questions about your results. This lets you analyze a moderate number of qualitative responses interactively.

The trade-off: While ChatGPT is handy for ad-hoc analysis, handling your survey data this way can be tedious—copying, pasting, chunking if you have a lot of responses, and manually managing your prompts. It’s a quick fix, but not ideal for more than a single round of analysis or team collaboration.

All-in-one tool like Specific

Platforms built for AI survey analysis, like Specific, take things further. Not only can you create and launch conversational surveys (which feel much more natural for online course students), but you also get built-in AI-powered analysis.

Specific collects richer data by asking targeted follow-up questions automatically. When you’re ready to analyze, it summarizes responses, extracts key themes, and lets you chat with AI about the results. No spreadsheets or exports required. You can even manage what context is sent to the AI for every thread of analysis.

According to recent reviews of AI survey tools for online course student feedback, solutions like Qualtrics and Looppanel offer similar features—advanced analytics, automated theme extraction, and workflow efficiencies that make qualitative analysis scalable and human-friendly for educators and program managers [1][2].

Useful prompts that you can use to analyze online course student likelihood to recommend surveys

To get the best results out of your AI tool (whether you use ChatGPT, Specific, or another GPT-based platform), mastering your prompts is a game changer. It helps you truly understand why students would (or wouldn’t) recommend your course. Here are the prompts I rely on:

Prompt for core ideas: This extract-themes prompt is great for any large set of open-ended student responses. It’s built into Specific, but it works anywhere:

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 works better the more context it has. Give the AI details like the survey’s purpose, student demographics, or what improvement goals you have. For example:

I am analyzing open-ended responses to a likelihood to recommend survey for online course students at a mid-sized university. The course is asynchronous, and my goal is to find out what factors drive high or low recommendations so I can improve next semester’s curriculum design.

After getting your core ideas, try:

“Tell me more about XYZ (core idea)” to explore specific feedback threads further.

Prompt for specific topic: When you want to fact-check, quickly ask:
“Did anyone talk about XYZ?”
Add “Include quotes” if you want direct student voices.

Prompt for pain points and challenges: Get the AI to list frustrations students mention, with an eye for patterns:

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: Spot how students feel about your course:

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 & ideas: Easily collect 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.

Prompt for unmet needs & opportunities: This will surface new ideas for improvement, straight from your students:

Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.

Want even more actionable prompts? Check out our guide to the best questions for online course student surveys about likelihood to recommend.

How Specific analyzes qualitative responses by question type

Let’s talk workflow. In Specific, the AI breaks down survey analysis according to the structure of your questions:

  • Open-ended questions (with or without follow-ups): You’ll get a complete summary of all responses and a separate summary for all follow-up responses. This means every free-text answer and clarification is captured and grouped.

  • Choice questions with follow-ups: For each choice, you get a separate summary of all the follow-up answers that relate to that specific choice. This helps you see why students picked a certain option and what details influenced them.

  • NPS (Net Promoter Score) format: Students are grouped as detractors, passives, or promoters. Each group’s follow-up responses are summarized on their own, making it easy to see what drives recommendations, indifference, or criticism.

You can apply these same analysis steps in ChatGPT or another AI, but you’ll need to do a bit more manual legwork to organize and segment responses. If you want a streamlined path, Specific was built for this exact use case.

If you want to learn how to easily create a student survey about likelihood to recommend, check our in-depth how-to guide.

Solving context limit problems in AI survey response analysis

Even the best AI models (including those in Specific and ChatGPT) have context size limits—if you paste in too many student survey responses, the model may ignore or truncate some. Here’s how to confidently analyze large volumes of feedback:

  • Filtering: In Specific, you can filter conversations—meaning only student threads that contain replies to a particular question or choice are sent to the AI for analysis. This keeps your context lean and highly targeted.

  • Cropping: You can crop the data, so only selected questions (like those about likelihood to recommend) are fed into the AI. This lets you cover more ground with less risk of losing nuance.

Both of these features are available out of the box in Specific, and they’re a lifesaver when you hit the limits of even the most advanced AI platforms. Other tools like Looppanel and Qualtrics handle this differently, but Specific’s approach is purpose-built for survey analysis [1][2].

Collaborative features for analyzing online course student survey responses

Analyzing survey results is rarely a solo mission. When several team members or instructors want to learn from online course students about their likelihood to recommend, collaboration is a must—but it can get messy without the right setup.

Chat-based AI analysis makes teamwork simple. In Specific, not only can you analyze survey data directly in a conversation with AI, but you can also launch multiple chats at once. Each chat can have its own filters and focus areas, letting different teammates dig into the same data from multiple angles.

Track contributions by team member. Every chat displays the creator’s name and avatar, so you always know who started each line of analysis. When collaborating in AI Chat, all messages show the sender’s avatar, keeping things transparent and organized for teams and educators working together on course improvements.

Seamless follow-up and sharing insights. Insights aren’t lost—they’re preserved for future reference, discussion, and reporting. Whether you’re refining curriculum or reporting to leadership, analysis stays structured and collaborative.

If your team wants to edit surveys and collaborate on design changes, check out our AI survey editor or jump straight into building a survey tailored for online course students.

Create your online course student survey about likelihood to recommend now

Start capturing next-level feedback with an AI-powered survey that analyzes responses on autopilot. Turn student voices into meaningful improvements in minutes, not days—no data wrangling needed.

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Sources

  1. Scijournal.org. Best online tools for student feedback and course evaluations.

  2. Nkmanandhar.com.np. 100 generative AI tools and platforms for educational research in 2025.

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.