Here are some of the best questions for an online course student survey about likelihood to recommend, plus tips for designing them efficiently. With Specific, you can easily generate a conversational survey in seconds to capture real student insights.
Best open-ended questions for online course student surveys about likelihood to recommend
Open-ended questions uncover detailed feedback, letting students express their true feelings and explain their ratings in their own words. These questions are perfect when you want context, stories, or reasons behind student behaviors, helping you understand what drives recommendations. Here are 10 top open-ended prompts we recommend asking online course students about their likelihood to recommend:
What is the main reason you would (or would not) recommend this course to others?
Can you describe your most valuable learning experience in this course?
What aspects of the course did you find most engaging or helpful?
If anything held you back from recommending the course, what was it?
How did the instructor or course content influence your likelihood to recommend?
What improvements would make you more likely to recommend this course in the future?
Were there any unexpected benefits you experienced as a result of taking this course?
How would you compare this course to other online courses you’ve taken?
What would you tell a friend considering enrolling in this course?
Is there something about the course experience that exceeded – or fell short of – your expectations?
Open-ended online course surveys not only give richer answers, but they help you discover what influences students most. With 70% of customers saying they’ll recommend a brand after a good experience, it’s crucial to hear firsthand what makes a lasting impression. [1]
Best single-select multiple-choice questions for online course student surveys about likelihood to recommend
Single-select multiple-choice questions make it easy to quantify results and identify big-picture patterns. They’re ideal when you want to track trends over time or spark a deeper conversation—sometimes it’s just easier for students to pick an option before diving into details. Here are three powerful examples you can adapt:
Question: How likely are you to recommend this course to a friend or colleague?
Very likely
Somewhat likely
Not likely
Question: What was the biggest factor in your decision to recommend (or not recommend) this course?
Course content quality
Instructor effectiveness
Learning platform experience
Other
Question: Prior to taking this course, had you recommended any online courses to others?
Yes
No
When to follow up with "why?" If a student selects “Not likely” for the likelihood to recommend, asking a follow-up "why?" can turn a closed answer into actionable feedback. For example, if someone chooses “Course content quality” as their biggest factor, you can ask: “Can you tell us more about which aspects of the content didn’t meet your needs?” This turns a box-checked response into insight you can actually use.
When and why to add the "Other" choice? “Other” opens the door for perspectives you may not expect. Adding it lets students voice a reason you hadn’t considered, and a smart follow-up—like “Please describe what else influenced your decision”—uncovers hidden drivers you might otherwise miss.
Keep in mind, students’ loyalty is highly influenced by their support experience: 73% say customer service affects whether they stick with an online learning brand. [1] Good, clear multiple-choice questions help you spot those underlying loyalty boosters.
NPS question for online course student surveys: does it make sense?
The Net Promoter Score (NPS) is a powerful, proven metric for measuring how likely your students are to recommend your course. The NPS question—“On a scale of 0–10, how likely are you to recommend this course to a friend or colleague?”—lets you quickly benchmark student satisfaction and track improvements over time. For online courses, NPS gets straight to the core of student loyalty and advocacy. It's especially valuable because promoters (those who score 9 or 10) can drive growth, while detractors (0–6) highlight issues needing attention. With Specific, launching an NPS survey for online course students is effortless, and you can automatically add smart, contextual follow-ups for richer answers.
The power of follow-up questions
We’ve seen that the magic of AI-powered follow-up questions is what makes online course student surveys more insightful. Follow-ups help clarify vague answers, explore the reasons behind responses, and probe for real-life examples—without manual back-and-forth. In fact, with AI-driven follow-ups, you will never miss the context that matters.
Specific’s AI asks smart, real-time follow-ups based on each student’s response, just like an expert interviewer. This means you get answers that are actually useful, and save time you’d otherwise spend chasing clarifications via email. It’s no wonder that 90% of student queries in e-learning can already be handled by AI chatbots today [2]—imagine that power driving your surveys!
Online course student: “It was ok, but I wish there were more examples.”
AI follow-up: “Can you tell us which topics could have benefited from additional examples, and how it would have helped your learning?”
How many follow-ups to ask? Usually, two or three follow-up questions are enough to get all the nuance you need. You can always let students skip to the next question once you have the detail you were looking for—Specific gives you control over this setting so your survey flows naturally and never feels overwhelming.
This makes it a conversational survey: follow-ups transform your survey into an engaging, chat-like experience, boosting participation and the quality of feedback.
AI survey analysis, summaries, insights: Don’t worry about drowning in text responses—a good AI-powered survey tool makes it a breeze to analyze survey responses, summarize patterns, and uncover key takeaways from open-ended feedback.
Automated follow-ups are a new way of surveying—try generating an AI-powered survey and see how much richer, faster, and friendlier this process can be.
How to prompt ChatGPT to come up with great questions for online course student surveys about likelihood to recommend
If you want to use AI tools like ChatGPT to help brainstorm questions for your online course student survey about likelihood to recommend, start with direct prompts, then gradually add more context. Here’s the simplest way to begin:
Suggest 10 open-ended questions for online course student survey about likelihood to recommend.
To get higher quality results, always provide more detail. For instance, explain your audience, the course’s subject, or your particular goals. Here’s a richer example prompt:
I am designing a survey for students who completed an online UX design course. The aim is to find out what makes them likely to recommend the course to others, and what areas we should improve to increase recommendations. Suggest 10 in-depth open-ended questions and 3 single-select multiple-choice questions to capture both emotional and practical feedback.
Once you have a draft list of questions, ask ChatGPT to categorize them for better structure:
Look at the questions and categorize them. Output categories with the questions under them.
Now, go deeper into categories that matter most to you. For example:
Generate 10 questions for categories "Perceived Value" and "Areas for Improvement".
This stepwise approach helps you build thoughtful, relevant questions, using AI as your creative partner.
What is a conversational survey?
A conversational survey feels like a dialogue—it adapts to each user’s responses, asks follow-up questions on the fly, and explores answers in real time. Unlike old-school “form” surveys that just collect data in boxes, an AI survey builder like Specific turns feedback collection into a genuine exchange. You don’t have to guess what to ask next; the AI uses context, follows logic, and uncovers the why behind the what.
Manual Surveys | AI-generated Surveys |
|---|---|
Static questions only | Dynamic follow-ups, adapts to response |
Requires manual analysis | AI summarizes responses and highlights insights |
Slow to build and iterate | Instant survey creation via chat |
Impersonal, form-like experience | Feels like a real conversation |
AI-powered conversational surveys give you nuanced, multi-layered data. For course creators and education teams, this means faster feedback loops, richer insights, and more actionable takeaways.
Why use AI for online course student surveys? Because AI-powered survey builders can reduce survey creation and analysis time by up to 40% [3], let you launch and adapt surveys instantly, and adaptively capture the full story behind every answer. AI survey examples, including those for likelihood to recommend, show how much more you can learn in less time and with less effort.
When you’re ready to discover just how easy and thorough this process can be, check out our complete guide on making a survey for online course students about recommendations, and see the AI survey generator in action. At Specific, we aim to make the conversational survey process smooth, engaging, and superior for every feedback need.
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