Q01
Message
All templatesPrivacy & Compliance
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.
Sample questions
A preview of what’s in the template. Every question is editable before you launch.
14 questions · ~7 min
Q02
Multiple Choice
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
Q03
Opinion ScaleRequired
Overall, how comfortable are you with AI tools monitoring or scoring the quality of your work?
Scale: 1 – 7
Min:Very uncomfortableMax:Very comfortable
Q04
MatrixRequired
How much do you agree or disagree with each statement about AI monitoring at your workplace?
5 rows × 5 columns
- 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
Columns: Strongly disagree · Disagree · Neutral · Agree · Strongly agree
Q05
Multiple ChoiceRequired
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
Q06
Best–Worst Trade-off (MaxDiff)Required
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
Pick best & worst per setBest:Concerns me mostWorst:Concerns me least
Q07
Rating ScaleRequired
How accurate do you think AI-generated quality scores are at capturing the real quality of your work?
Range: 1 – 5
Min:Not at all accurateMax:Extremely accurate
Q08
AI Interview
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.
Q09
Multiple ChoiceRequired
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
Q10
Long Text
What would need to change about how AI monitoring works for it to feel fair to you?
Q11
Multiple Choice
Which best describes your role?
- Individual contributor
- People manager
- Senior leader / executive
- Contractor / contingent worker
- Prefer not to say
Q12
Multiple Choice
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
Q13
Multiple Choice
Which age group do you belong to?
- Under 25
- 25-34
- 35-44
- 45-54
- 55-64
- 65 or older
- Prefer not to say
Q14
Message
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.
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.
How it compares
We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.
Why this template
- 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.
What it does well
- 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
Where it falls short
- 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
Ready to launch?
Open this template in the editor. Every part is yours to change before the first respondent sees it.
Related templates
More studies from the same category.
Privacy & Compliance
Research Informed Consent & Enrollment
A complete consent flow for research studies: plain-language study information, granular consent statements with a comprehension check, signature capture, and contact enrollment. Built for IRB-style requirements — participants confirm they understand, not just that they scrolled.
View templatePrivacy & Compliance
Voice Authentication in Banking: Adoption & Trust Survey
Measures awareness, security perceptions, usage experience, and adoption intent for voice biometric authentication among banking customers. Suitable for financial institutions evaluating customer readiness for voice-based identity verification.
View templatePrivacy & Compliance
First-Session Trust Signals & Onboarding Survey
Measures which trust cues, social proof elements, and onboarding flows most effectively build user confidence during first-time website and app visits. Designed for UX researchers and product teams optimizing conversion funnels.
View templatePrivacy & Compliance
Third-Party Risk Disclosure Clarity Assessment
Evaluates how clearly third-party risk disclosures and mitigations are communicated across teams. Designed for GRC, security, procurement, and other stakeholders who interact with vendor risk information, this instrument identifies gaps in accessibility, readability, and actionability to drive targeted improvements.
View templatePrivacy & Compliance
Smart Home Control Preferences & Default Settings Study
Surveys homeowners on their smart home comfort levels, device control preferences, and attitudes toward manufacturer default settings to inform product UX and setup experience design.
View templatePrivacy & Compliance
Privacy Nutrition Label Comprehension & Trust Survey
Measures how users interpret, evaluate, and act on app privacy nutrition labels using a concept-testing methodology with comprehension checks, trust and usefulness scales, and preference ranking. Suitable for UX researchers, privacy teams, and compliance professionals seeking to optimize label design.
View template