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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.

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Sample questions

A preview of what’s in the template. Every question is editable before you launch.

14 questions · ~7 min
Q01
Message

Thanks for taking a few minutes to share how you feel about AI tools that monitor or score the quality of your work. There are no right or wrong answers here — honest input helps shape fairer policies. About 5-6 minutes.

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 Template

This 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

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