모든 템플릿

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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질문 15개 · 약 8분
Q01
메시지

Thanks for taking a few minutes on this! We're studying how much work it takes to review AI agent outputs before you trust and use them. Your responses are completely confidential and anonymized. Honest answers help us reduce your review burden. About 5-6 minutes.

Q02
객관식필수

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
객관식필수

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
의견 척도필수

When this agent gives you an output, how much do you currently trust it to be correct without checking?

척도: 17
최소:Don't trust it at all최대:Fully trust it
Q05
의견 척도필수

How much of the agent's output do you actually review or verify before using it, on average?

척도: 010
최소:None of it최대:All of it, line by line
Q06
슬라이더 매트릭스

Rate the effort each of the following review activities takes, on a typical task.

4개 행, 각 행마다 슬라이더 1개
  • 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
슬라이더 0–10최소:No extra effort최대:Extremely high effort
Q07
매트릭스

How often does each of these happen with this agent's output?

4개 행 × 5개 열
  • 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
: Never · Rarely · Sometimes · Often · Almost every time
Q08
객관식

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
순위 매기기

Rank these factors by how much they currently increase your review burden, from most to least.

  1. Output is hard to verify against a clear source of truth
  2. Agent doesn't explain its reasoning or show its work
  3. Errors in the past have been high-impact when they happened
  4. Task volume is too high to check everything carefully
  5. Unclear who is accountable if the output is wrong
드래그하여 순위 지정
Q10
의견 척도필수

Overall, is the current level of review you do on this agent's output too much, too little, or about right?

척도: 15
최소:Way too much review최대:Way too little review
Q11
AI 인터뷰

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
메시지

Last few questions are about you, totally optional.

Q13
객관식

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
객관식

Which best describes your role?

  • Individual contributor
  • Team lead / manager
  • Director or above
  • Other
  • Prefer not to say
Q15
메시지

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

    정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.

  • 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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