Templates / Course feedback survey
Research · Adaptive interview template
Course feedback survey template
Course feedback forms measure satisfaction and miss learning. "Rate the instructor," "rate the materials," a five-star average, and a smiley face at the end. None of it tells you whether anyone can now do the thing the course was supposed to teach, or which module quietly lost the room.
This template treats a course, workshop, or training session as something to interview learners about, not just rate. A satisfaction anchor sets the tone, then the AI interviewer digs into what will actually stick: which part was most useful, where the pace lost them, what they expected to learn but did not, and what they will apply on Monday. Whether it is a live workshop, an async video course, or an internal training, you hear what worked in the learner's own words.
When to use it
- Immediately after a live workshop or training session, while it is fresh
- At the end of a self-paced online course, per module or overall
- For recurring internal training, to compare cohorts over time
- When completion is high but application is low and you need to know why
Running it well
- Ask about application, not just satisfaction. "What will you use on Monday" separates a course people enjoyed from one that actually changed what they can do.
- Send it while it is fresh. A workshop survey a week later measures memory of the workshop, not the workshop.
- Ask about the goal they arrived with. A course can be excellent and still miss the learner, and the gap between expectation and content is your clearest signal.
- For multi-cohort training, keep the anchor question identical so you can compare sessions and instructors fairly over time.
The questions, and why they're shaped this way
These are the anchor questions. You can edit, reorder, or add to them; the AI interviewer handles the depth between them.
How would you rate this course overall?
A single overall rating anchors the learner's reaction and tells the interviewer whether to chase what worked or what fell short. It is a starting point for the interview, not the evaluation itself.
What is one thing you'd change about this course?
Asking for one change beats "any feedback?" because it forces a specific, prioritized answer. The follow-ups then dig into whether it is pacing, content, or delivery behind the request.
What the interviewer asks next
Follow-ups aren't scripted. The AI reads each answer and probes what a researcher would. Depending on what a respondent says, it might ask:
"You said the middle section dragged. Was it the pace, the depth, or the examples that lost you there?"
"You rated it excellent. Which part will you actually use, and when is the first time you expect to use it?"
"You came in wanting to learn one specific thing. Did the course get you there, and if not, where did it stop short?"
Live demo
Play the course feedback survey interview
This is the real interviewer, not a recording. Answer as yourself and watch the follow-ups adapt to what you write.
Prefer a full window? Open the demo.
Common questions
- Does this work for live workshops and self-paced courses?
- Both. Send it right after a live session for freshness, or at the end of an online course or per module. The follow-ups adapt to whatever the learner just experienced.
- How is this better than a star-rating form?
- A star rating tells you people were satisfied. This interviews them: the AI asks which part was useful, where the pace missed, and what they will apply, so you learn whether the course actually taught, not just pleased.
- Can learners answer in their own language?
- Yes. The template supports 7 languages, so a learner can take the interview in the language they think in, which usually produces more specific and honest feedback.
- What do the results look like?
- The overall rating breakdown, themes with supporting quotes and counts, full transcripts, and a plain-language question box for asking things like "which module did learners most often want changed?"
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