Q01
Message
All templates
AI Agent Output Review Burden and Trust Calibration Survey
Measures how much time and cognitive effort employees spend checking AI agent outputs, where trust is over- or under-calibrated, and what triggers a full manual re-check. An AI follow-up probes the last time output was wrong or nearly acted on unchecked.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
15 questions · ~8 min
Q02
Multiple ChoiceRequired
Which AI agent or tool do you use most often in your work?
- (Replace with Agent A)
- (Replace with Agent B)
- (Replace with Agent C)
- Other
Q03
Multiple ChoiceRequired
In the last 30 days, how often did you use this agent's output?
- Multiple times a day
- Once a day
- A few times a week
- Once a week or less
Q04
Opinion ScaleRequired
When this agent gives you an output, how much do you currently trust it to be correct without checking?
Scale: 1 – 7
Min:Don't trust it at allMax:Fully trust it
Q05
Opinion ScaleRequired
How much of the agent's output do you actually review or verify before using it, on average?
Scale: 0 – 10
Min:None of itMax:All of it, line by line
Q06
Slider Matrix
Rate the effort each of the following review activities takes, on a typical task.
4 rows, one slider each
- Re-reading the output for factual accuracy
- Cross-checking against source data or documents
- Re-running or testing the output yourself
- Getting a second person to check it
Slider 0–10Min:No extra effortMax:Extremely high effort
Q07
Matrix
How often does each of these happen with this agent's output?
4 rows × 5 columns
- Output contains a factual error I catch before using it
- Output contains an error I only catch later or after acting on it
- Output is correct but I still double-check it out of habit
- I skip reviewing entirely because I trust it
Columns: Never · Rarely · Sometimes · Often · Almost every time
Q08
Multiple Choice
What most often triggers you to do a full manual re-check instead of a quick glance?
- The task is high-stakes (money, legal, customer-facing)
- The output looks unusual or inconsistent with what I expected
- I've been burned by an error from this agent before
- It's a new or unfamiliar type of task
- A colleague or policy requires it
- I always do a full check regardless
Q09
Ranking
Rank these factors by how much they currently increase your review burden, from most to least.
- Output is hard to verify against a clear source of truth
- Agent doesn't explain its reasoning or show its work
- Errors in the past have been high-impact when they happened
- Task volume is too high to check everything carefully
- Unclear who is accountable if the output is wrong
Drag to rank
Q10
Opinion ScaleRequired
Overall, is the current level of review you do on this agent's output too much, too little, or about right?
Scale: 1 – 5
Min:Way too much reviewMax:Way too little review
Q11
AI Interview
Reconstruct the most recent specific instance where the respondent's trust in this agent's output was wrong in either direction: a time an error slipped through with too little review, or a time they over-reviewed something that turned out fine. Get concrete details on what the output was, what checking they did or skipped, what happened as a result, and how that changed their review habits afterward. If they say they always fully check everything, probe whether that's sustainable given their task volume and what would let them safely check less.
Q12
Message
Last few questions are about you, totally optional.
Q13
Multiple Choice
How long have you been using AI agent tools in your work?
- Less than 3 months
- 3-12 months
- 1-2 years
- More than 2 years
- Prefer not to say
Q14
Multiple Choice
Which best describes your role?
- Individual contributor
- Team lead / manager
- Director or above
- Other
- Prefer not to say
Q15
Message
All done — thank you! Your answers feed directly into a report on where AI agent review effort can be safely reduced and where trust needs stronger guardrails.
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.
Why this template
What this template is built to do — we found no directly comparable template from other survey tools to review.
What sets it apart
- Includes an AI follow-up interview that reconstructs the respondent's most recent specific instance of misplaced trust or a near-miss where wrong output was almost acted on unchecked, going beyond static rating scales
- Combines opinion-scale trust and verification-effort questions with a slider-matrix rating the effort of specific review activities, capturing both perception and behavior
- Uses a matrix and ranking question to surface which failure patterns and review-burden factors occur most often and matter most, not just a single satisfaction score
- Closes with an automatically generated report structure plus transparent, inspectable prompts, so methodology isn't a black box
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.