Post-Lecture Feedback & Teaching Quality Survey

Captures how clear, well-paced, and engaging a lecture was, plus which specific aspects (pacing, examples, Q&A, materials) students most want improved. An AI follow-up interview digs into a concrete moment the student found confusing or engaging, turning vague 'it was fine' ratings into specifics an instructor can act on.

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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

11 questions · ~6 min

Q01

Message

Hi! We'd love your honest feedback on the lecture you just attended (or watched). It takes about 7 minutes and helps the instructor improve future sessions.

Q02

Multiple ChoiceRequired

In the past month, how many of this course's lectures have you attended in person or watched live?

All of them
Most of them
About half
A few
None

Q03

Opinion ScaleRequired

How likely are you to recommend this lecture to a classmate who's deciding whether to attend?

Scale: 0 – 10

Min:Not at all likelyMax:Extremely likely

Q04

MatrixRequired

How much do you agree with each statement about this lecture?

5 rows × 5 columns

The pace of the lecture was appropriate
The instructor explained concepts clearly
The examples used helped my understanding
The slides or visual materials supported learning
The instructor encouraged questions and discussion

Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree

Q05

Rating Scale

How would you rate the instructor's responsiveness when you asked questions during or after the lecture?

Range: 1 – 5

Min:PoorMax:Excellent

Q06

Best–Worst Trade-off (MaxDiff)Required

If the instructor could only change a few things about this lecture, which would matter most to you?

More real-world examples
More time for questions and discussion
Clearer explanations of core concepts
Better structured or paced content
More engaging delivery style
Improved slides or visual aids
More opportunities for hands-on practice

Pick best & worst per setBest:Would improve the lecture mostWorst:Would improve the lecture least

Q07

AI Interview

Ask the respondent to describe a specific moment in the lecture where they felt most engaged or most lost, anchoring on whichever statement they rated lowest in the pacing/clarity/examples battery. Get concrete detail: what was being explained at that point, what confused them or made it click, and what the instructor could have done differently right there. If they flagged a top improvement area in the trade-off question, ask them to describe what a fixed version of that lecture would actually look like.

Q08

Long Text

Is there anything else about this lecture you'd like to share with the instructor or course organizers?

Q09

Multiple Choice

What is your current year or level of study?

First year
Second year
Third year
Fourth year or above
Graduate student
Prefer not to say

Q10

Multiple Choice

What is your field of study or department? (Template note: replace the list below with your institution's actual departments before launching.)

(Replace with Department A)
(Replace with Department B)
(Replace with Department C)
Other
Prefer not to say

Q11

Message

Thanks so much for the feedback! Your responses are combined with other students' answers into a summary report the instructor uses to improve future lectures — individual answers are never shared with a name attached.

What’s included

AI follow-ups
Adaptive probes on open-ended answers that pull out detail a static form would miss.
Attention checks
Built-in safeguards against rushed answers and low-quality respondents.
AI-drafted copy
Wording, ordering, and branching written by the AI — tuned to your research goal.
Auto report
Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

Combines quick quantitative pulses (pacing/clarity opinion scale, agreement matrix, instructor responsiveness rating) with a max-diff question that forces students to prioritize which single change matters most
Includes an AI follow-up interview that asks the student to describe one specific confusing or engaging moment, turning generic 'it was fine' answers into concrete, actionable detail for the instructor
Bookends the survey with friendly chat-style intro/outro messages and closes with an open long-text field so students can add anything the structured questions missed
Captures context (attendance frequency, year of study, field/department) so instructors can see if feedback patterns differ by student segment

SurveyMonkey

Teaching Assistant Evaluation: TA Survey Examples

This is a ready-to-use template on an established survey platform, aimed at evaluating teaching assistants rather than lecture-specific instructor performance, so it's a related but not identical use case. It likely covers standard rating and open-text questions typical of SurveyMonkey's education templates, with the platform's usual reporting and distribution tools. No AI-driven probing or per-response scoring is part of the offering.

What it does well

Backed by a well-known, mature survey platform with broad distribution and analytics tooling
Purpose-built for academic/teaching evaluation context, so question framing is likely relevant to instructors
Ready to deploy without configuration, useful for a quick baseline TA evaluation

Where it falls short

Static question set with no adaptive AI follow-up to probe vague answers like 'it was fine'
No voice AI interview option or guided screen-share tasks
No transparent, published prompt/methodology for how responses are interpreted or scored

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