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.
샘플 질문
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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
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
When this agent gives you an output, how much do you currently trust it to be correct without checking?
How much of the agent's output do you actually review or verify before using it, on average?
Rate the effort each of the following review activities takes, on a typical task.
- 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
How often does each of these happen with this agent's output?
- 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
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
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
Overall, is the current level of review you do on this agent's output too much, too little, or about right?
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.
Last few questions are about you, totally optional.
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
Which best describes your role?
- Individual contributor
- Team lead / manager
- Director or above
- Other
- Prefer not to say
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.
포함된 기능
AI 후속 질문
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주의력 확인 장치
성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.
AI가 작성한 문안
문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.
자동 리포트
응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.
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차별화 포인트
- 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
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