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Professor Teaching Effectiveness Evaluation Survey

Measures how students experience a professor's teaching across clarity, engagement, feedback, availability, and fairness, plus overall satisfaction and likelihood to recommend. Built for academic departments and course coordinators running end-of-term reviews, with an AI follow-up interview that digs into the specific moment or assignment that most shaped a student's rating.

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Sample questions

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

12 questions · ~7 min
Q01
Message

Thanks for taking a few minutes to reflect on this course and instructor. Your honest feedback helps shape how the course is taught in the future. This should take about 5 minutes, and your responses will be reviewed anonymously.

Q02
Multiple ChoiceRequired

Which best describes your attendance in this course this term?

  • Attended nearly every class
  • Attended most classes
  • Attended about half of classes
  • Attended less than half of classes
  • Rarely attended
Q03
MatrixRequired

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

6 rows × 5 columns
  • Explains concepts clearly
  • Encourages questions and participation
  • Provides timely, useful feedback on assignments
  • Is available and responsive outside of class (office hours, email)
  • Grades fairly and consistently
  • +1 more
Columns: Strongly Disagree · Disagree · Neutral · Agree · Strongly Agree
Q04
Multiple Choice

In the last month, how many times did you use this professor's office hours or scheduled availability?

  • Never
  • Once
  • 2-3 times
  • 4 or more times
Q05
Opinion ScaleRequired

Overall, how effective is this professor at helping you learn the material?

Scale: 1 – 7
Min:Not effective at allMax:Extremely effective
Q06
Opinion ScaleRequired

How likely are you to recommend this professor's course to another student?

Scale: 0 – 10
Min:Not at all likelyMax:Extremely likely
Q07
Best–Worst Trade-off (MaxDiff)

Which of these changes would improve this course the most, and which would matter least?

  • Faster turnaround on grading
  • More real-world examples and applications
  • More interactive discussion or activities in class
  • Clearer syllabus and assignment expectations
  • More availability outside of class
  • Updated or more current course materials
  • Smaller-group or peer collaboration time
Pick best & worst per setBest:Would help mostWorst:Would help least
Q08
AI Interview

Probe the reasoning behind the respondent's overall effectiveness rating, anchoring on the specific teaching dimension (clarity, feedback, availability, or fairness) they rated lowest or found most notable. Ask for a concrete example — a specific class, assignment, or interaction — that shaped their view, and what the professor could have done differently in that moment. If the respondent rated everything highly, ask what single thing this professor does that other instructors should copy.

Q09
Long Text

Is there anything else about this professor or course you'd like to share?

Q10
Multiple Choice

What is your class standing?

  • Freshman
  • Sophomore
  • Junior
  • Senior
  • Graduate student
  • Other
  • Prefer not to say
Q11
Multiple Choice

Was this course required for your major or degree program?

  • Yes, required
  • No, elective
  • Not sure
  • Prefer not to say
Q12
Message

Thank you for sharing your feedback! Your responses will be combined anonymously with other students' answers and shared with the department to support the professor's growth and course improvements.

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

  • Includes a matrix question covering clarity, engagement, feedback, availability, and fairness alongside separate opinion-scale ratings for overall teaching effectiveness and likelihood to recommend
  • Pairs quantitative ratings with an AI follow-up interview that probes the specific moment or assignment that most shaped the student's overall effectiveness rating, going beyond a static open-text box
  • Captures context like attendance, office-hours usage, class standing, and whether the course was required, so departments can segment results by student engagement level
  • Uses a max-diff question to prioritize which course changes would matter most, giving coordinators a ranked action list rather than just averaged scores

QuestionPro

University Faculty Satisfaction Survey Template

A fielding-ready static survey template aimed squarely at university faculty/course evaluation, so it's a direct comparable. It covers standard satisfaction and rating dimensions typical of end-of-term instructor reviews. It does not appear to offer any adaptive follow-up questioning beyond the fixed question set.

What it does well

  • Purpose-built for academic faculty evaluation use cases
  • Backed by an established survey platform with broad question-type support
  • Likely offers customizable rating scales common to course evaluations

Where it falls short

  • No adaptive AI follow-up interview to explore the reasoning behind a rating
  • No mention of voice-based interviews or guided screen-share tasks
  • No transparent, inspectable AI prompts or automated per-response quality scoring

SurveyMonkey

Teaching Assistant Evaluation: TA Survey Examples

This template targets teaching assistant evaluation rather than professor evaluation specifically, but it's close enough in structure and audience (academic course feedback) to be a reasonable comparison. It's a static, fielding-ready form built for end-of-term academic feedback collection. It focuses on a fixed set of questions with no dynamic follow-up capability described.

What it does well

  • Built specifically for academic teaching feedback contexts
  • Comes from a widely used survey platform with strong reporting/export features
  • Likely includes example questions departments can adapt quickly

Where it falls short

  • Designed for TA rather than professor-level evaluation, so dimensions like long-term teaching fairness may be less developed
  • No adaptive AI or voice interview component to dig into individual student experiences
  • No published methodology for how responses are scored or interpreted

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.

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