Measure What Really Drives
AI Adoption
Go beyond accuracy. Benchmark trust, fairness, and transparency with the AI-UX Self-Assessment Questionnaire.
Why It Matters
Technical performance alone doesn't guarantee adoption. The AI-UX Questionnaire helps you uncover the human side of AI experiences across 7 critical dimensions.
- TrustTrust is the foundation of any successful AI system. It’s not enough for users to tolerate AI — they must believe it acts reliably, predictably, and in alignment with their goals and expectations.
- TransparencyUsers don’t trust what they can’t understand. Transparency isn’t just a matter of disclosure — it’s about making the system’s purpose, behavior, and boundaries understandable.
- FairnessFairness is not just a model property — it’s a user experience. People assess whether an AI is fair not only based on outcomes, but on perceived treatment, inclusion, and bias.
- Agency & ControlEffective AI should augment — not override — users. When people feel that decisions are being made for them without their input, the experience quickly shifts from empowering to alienating.
- AccountabilityUsers need to know that there are people — not just systems — behind the AI. Accountability is about creating clear feedback loops, escalation paths, and responsibility structures.
- Privacy & Data PracticesAI systems increasingly rely on personal, behavioral, or contextual data. But users won’t share if they don’t feel safe.
- Perceived Quality & Ease of UseEven technically advanced AI systems will struggle to gain traction if they’re confusing, frustrating, or fail to meet user expectations in real-world settings. But ease of use alone isn’t enough.
Gather responses and calculate results
Compare results and take action
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Send the questionnaire to your users
How It Works ?
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Three simple steps to measure AI user experience.
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🎯 Perfect for
- AI product teams
- UX researchers and product designers
- Responsible AI and ethics leads
- Innovation or strategy leads
- Anyone building human-centered AI systems
- Consultants building human-centered AI audits
AI-UX Self-Assessmente Questionnaire
Deliverable - PDF file
6.90
€
⭐ Evaluate, Communicate, and Benchmark AI User Experiences with Confidence.
The questionnaire focuses on 7 critical dimensions that influence the success and adoption of AI systems:
The questionnaire focuses on 7 critical dimensions that influence the success and adoption of AI systems:
- Trust
- Transparency
- Agency & Control
- Accountability
- Fairness
- Privacy & Data Practices
- Perceived Quality & Ease of Use
AI-UX Self-Assessment Questionnaire Notion Toolkit
Deliverable - Notion Link with Questionnaire + Excel Calculator + Guides
49.00
€
✅ What’s Inside
- Questionnaire: 23 items grouped into 7 dimensions of AI user experience
- Excel Calculator: Automated calculations and ready-to-use graphs for reports and presentations.
- Scoring Guide: Step-by-step instructions to calculate results
- Score Interpretation: Labels and thresholds to make sense of your scores
- FAQ section: Provides guidance on more technical questions about qualitative research.
- Usage Terms & License: Understan
AI-UX Self-Assessment Questionnaire Calculator
Deliverable - Excel File
29.00
€
✅ What’s Inside
- How to Use – Quick guide to get started.
- Data – Paste in responses (handles up to 1,000 participants + missing data + demographics).
- AI-UX Calculation – Automatic scoring, deviations, and outlier checks.
- Study Results – Dashboard with overall score, grade, and dimension breakdowns.
- Answers Distribution – Question-level results for deeper analysis.
Join the Global Benchmark
Your data can shape the future of AI user experience.
Contribute anonymized results to help build the first global AI-UX benchmark.
Submit your score ↗ Contribute anonymized results to help build the first global AI-UX benchmark.
FAQs
The AI-UX Self-Assessment Questionnaire is a diagnostic tool that helps organizations measure the perceived quality of AI-powered user experiences.
It was designed to capture what technical metrics can’t: how users feel about interacting with an AI system—whether they trust it, understand it, feel empowered by it, and believe it treats them fairly.
It was designed to capture what technical metrics can’t: how users feel about interacting with an AI system—whether they trust it, understand it, feel empowered by it, and believe it treats them fairly.
In a world where AI is becoming embedded into everything from healthcare to banking, user trust isn’t a luxury—it’s a requirement.
The AI-UX Self-Assessment Questionnaire works by asking users to rate their experience with an AI system across seven key dimensions—such as trust, transparency, and fairness—using simple, statement-based prompts.
Each dimension includes 3–5 Likert-scale items (e.g., “I trust the recommendations made by the system”) that capture how users perceive and interact with the AI in real-world contexts.
Each dimension includes 3–5 Likert-scale items (e.g., “I trust the recommendations made by the system”) that capture how users perceive and interact with the AI in real-world contexts.
Once responses are collected, you can process the scores in this calculator and analyze the scores to identify strengths, surface experience gaps, and guide future improvements.
The AI-UX Questionnaire measures how users perceive the quality, reliability, and integrity of their experience with an AI system.
It evaluates seven critical dimensions—Trust, Transparency, Agency & Control, Accountability, Fairness, Privacy & Data Practices, and Perceived Quality & Ease of Use—to capture how well the system aligns with human expectations, supports autonomy, and fosters long-term engagement. Together, these dimensions provide a holistic view of the system’s impact on user confidence, satisfaction, and ethical acceptance.
You should run an AI-UX Questionnaire study at key moments when you're looking to evaluate or improve the user experience of an AI-powered system.
This includes:
This includes:
- Before or after launching a new AI feature, to gather feedback on user perceptions and readiness.
- During usability testing, to supplement qualitative insights with structured perception data.
- At regular intervals, to monitor how trust, control, and satisfaction evolve over time.
- After major updates, especially if they affect automation, personalization, or decision-making logic.
- When adoption is low or user feedback is unclear, to identify hidden friction points or trust barriers.
- ProsStructured Insight Across 7 Key Dimensions: Measures critical aspects like trust, transparency, control, and fairness—offering a holistic view of user perception.
- User-Centered Diagnostic Tool: Captures subjective experience, helping teams understand how users feel, not just what they do.
- Actionable and Benchmarkable: Generates scores that can be tracked over time or compared across products and user groups.
- Flexible Format: Available in PDF, Google Forms, and Tally, making it easy to embed in any workflow or research stack.
- Non-Invasive and Easy to Deploy: Short and straightforward for users to complete, typically taking under 5 minutes.
- ConsRelies on Self-Reported Data: Subjective perceptions may not always align with actual behavior or system performance.
- Requires Contextual Interpretation: Results need to be read in relation to system type, user group, and maturity level—there are no universal "good" scores (yet).
- Not a Substitute for Usability Testing: Best used alongside other UX methods (like interviews or behavior tracking) rather than in isolation.
- Benchmarking Still in Progress: While the tool supports score tracking, industry-wide benchmarking is still being developed.
Yes, several external and contextual factors can influence the AI-UX Questionnaire score beyond the system’s actual design or performance. These include:
- User Expectations and Prior Experience: Users with past negative experiences with AI—or very high expectations—may rate a system more critically, regardless of actual performance.
- System Maturity: Early-stage or experimental AI systems may receive lower scores due to bugs, limited capabilities, or a lack of refinement.
- Use Case and Domain Sensitivity: AI in high-stakes domains (e.g., healthcare, finance) tends to be judged more strictly than in casual or low-risk applications (e.g., entertainment, shopping).
- Cultural and Demographic Differences: Perceptions of trust, fairness, and privacy vary across cultural and social contexts, influencing how users respond.
- Communication and Onboarding: If the system is poorly introduced or lacks clear explanations, users may feel confused or skeptical—even if the AI performs well.
- Device and Interface Constraints: A good AI experience on one platform (e.g., desktop) may not translate to another (e.g., mobile), skewing scores based on access channel.
- Recent Incidents or Media Coverage: Public scandals or news about AI misuse can prime users to distrust AI in general, impacting their answers even if your system is compliant and ethical.
