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.
設問の例
テンプレートの内容をプレビューできます。すべての設問は公開前に自由に編集できます。
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.
含まれる機能
AIによる深掘り
自由回答に合わせてAIが追加で質問し、固定のフォームでは拾えない具体的な内容を引き出します。
注意確認設問
急いだ回答や質の低い回答者を除外する仕組みを標準で備えています。
AIが作成する設問文
文言、設問の順序、条件分岐をAIが調査の目的に合わせて作成します。
自動レポート
回答が集まると、テーマ、引用、わかりやすい要約が自動で作成されます。
このテンプレートを選ぶ理由
このテンプレートの設計意図をご紹介します。ほかのアンケートツールには、直接比較できるテンプレートが見つかりませんでした。
ここが違う
- 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
よくあるご質問
「AI Agent Output Review Burden and Trust Calibration Survey」テンプレートにはどのような設問が含まれていますか?
すぐに使える設問が15問含まれており、最初の設問は次のとおりです:「Thanks for taking a few minutes on this! We're studying how much work it takes to review AI agent outputs before you tru…」・「Which AI agent or tool do you use most often in your work?」・「In the last 30 days, how often did you use this agent's output?」。すべての設問は上でプレビューでき、自由に編集できます。
このアンケートの回答にはどのくらい時間がかかりますか?
回答者は通常、15問を約8分で回答し終えます。
テンプレートは編集できますか?
はい。公開前であれば、すべての設問、選択肢、順序を編集できます。設問の追加や削除のほか、調査の目的に合わせた作り直しをAIエディターに依頼することもできます。
このテンプレートは無料で使えますか?
はい。エディターで開けば、すぐに編集を始められます。お試しにアカウントは不要で、無料プランでアンケートを公開できます。
公開の準備はできましたか?
このテンプレートをエディターで開いてみてください。最初の回答者が目にする前に、すべてを自由に変更できます。
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