AI Monitoring of Work Quality: Employee Attitudes Survey
Measures how employees feel about AI-based monitoring and quality-scoring tools at work — covering comfort, trust, fairness, and behavior change — with an AI follow-up that reconstructs one specific moment monitoring affected them instead of abstract opinions. Built for HR, legal, and workplace-technology teams evaluating or rolling out AI monitoring practices.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
Which of the following AI-based monitoring or quality-tracking tools are used in your current role, as far as you know?
- Screen or activity recording
- Keystroke or mouse-movement tracking
- AI-generated productivity or quality scores
- Automated review of emails, chats, or calls
- AI transcription or sentiment analysis of meetings
- Location or badge tracking
Overall, how comfortable are you with AI tools monitoring or scoring the quality of your work?
How much do you agree or disagree with each statement about AI monitoring at your workplace?
- AI monitoring gives an accurate picture of the quality of my work
- AI monitoring is applied more fairly than human review would be
- Knowing I'm monitored by AI increases my stress at work
- I trust decisions that are based on AI monitoring data
- AI monitoring has helped me improve my performance
In the last 30 days, how often have you changed what you were doing because you knew AI monitoring was active?
- Never
- Rarely (1-2 times)
- Sometimes (about weekly)
- Often (several times a week)
- Every day
Of the concerns below about AI monitoring of work quality, which matters most to you and which matters least?
- The accuracy of what AI monitoring measures
- Lack of transparency about how it's used
- Fear it could affect my job security or pay
- Loss of privacy in how I work
- Potential bias against certain people or teams
- Loss of control over how my work is judged
- Being reduced to a score without human context
- Where and how the data is stored or shared
How accurate do you think AI-generated quality scores are at capturing the real quality of your work?
Reconstruct one specific recent moment when AI monitoring or an AI-generated quality score affected the respondent — what it flagged or measured, how they found out, and what they did next. Anchor on their comfort and trust ratings: if they're uncomfortable or distrustful, dig into the exact moment trust broke down and what evidence would rebuild it; if they're comfortable, probe whether that's based on real experience or just assumption. If they say monitoring has never directly affected them, ask what they've heard from coworkers instead.
Would you support or oppose expanding AI monitoring to cover more parts of your job than it currently does?
- Strongly oppose
- Oppose
- Neutral
- Support
- Strongly support
What would need to change about how AI monitoring works for it to feel fair to you?
Which best describes your role?
- Individual contributor
- People manager
- Senior leader / executive
- Contractor / contingent worker
- Prefer not to say
How long have you worked at your current organization?
- Less than 1 year
- 1-3 years
- 4-7 years
- 8+ years
- Prefer not to say
Which age group do you belong to?
- Under 25
- 25-34
- 35-44
- 45-54
- 55-64
- 65 or older
- Prefer not to say
Thank you for sharing your honest perspective on AI monitoring at work. Your responses will be combined with others' to help shape fair, transparent monitoring policies — individual answers are kept confidential.
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.
다른 서비스와 비교
다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.
이 템플릿을 선택하는 이유
- Includes a matrix question measuring agreement across multiple statements on trust, fairness, and comfort with AI monitoring in one pass, rather than a single generic satisfaction score.
- Uses a max-diff exercise to force-rank which AI monitoring concerns (e.g., accuracy, surveillance, fairness) matter most, giving HR/legal teams prioritized data instead of open-ended complaints.
- Pairs a rating question on perceived accuracy of AI-generated quality scores with an AI follow-up interview that reconstructs one specific recent moment monitoring affected the employee — surfacing concrete incidents, not abstract opinions.
- Closes with a long-text question on what would need to change for AI monitoring to feel fair, plus role, tenure, and age demographics, so responses can be segmented by workforce group for rollout decisions.
SurveyMonkey
Employee Workload Survey TemplateThis is a static, fielding-ready template for gauging employee workload and stress, not AI monitoring or quality-scoring attitudes specifically. It's a reasonable comparison as a general-purpose employee sentiment survey on a mature platform, but it doesn't touch the AI-monitoring subject matter at all. Teams would need to heavily rebuild it to study monitoring trust or fairness.
잘하는 점
- Backed by an established, widely-used survey platform with broad distribution and reporting tooling
- Likely offers pre-built question logic and simple deployment for HR teams
- Familiar format that respondents and HR stakeholders already trust
아쉬운 점
- Doesn't address AI monitoring, quality scoring, or algorithmic management at all — off-topic for this use case
- Static question set with no adaptive AI follow-up to probe specific incidents
- No mechanism to surface a concrete moment of impact — relies on abstract Likert-style ratings only
설문을 공개할 준비가 되셨나요?
이 템플릿을 편집기에서 열어 보세요. 첫 응답자가 보기 전에 모든 부분을 원하는 대로 바꿀 수 있습니다.