Evaluating User Interfaces

⏱️ How To Measure UX (https://lnkd.in/e5ueDtZY), a practical guide on how to use UX benchmarking, SUS, SUPR-Q, UMUX-LITE, CES, UEQ to eliminate bias and gather statistically reliable results — with useful templates and resources. By Roman Videnov. Measuring UX is mostly about showing cause and effect. Of course, management wants to do more of what has already worked — and it typically wants to see ROI > 5%. But the return is more than just increased revenue. It’s also reduced costs, expenses and mitigated risk. And UX is an incredibly affordable yet impactful way to achieve it. Good design decisions are intentional. They aren’t guesses or personal preferences. They are deliberate and measurable. Over the last years, I’ve been setting ups design KPIs in teams to inform and guide design decisions. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < 60s (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 80% (usage of a new feature per user) 10. Time to pricing quote < 2 weeks (for B2B systems) 11. Application processing time < 2 weeks (online banking) 12. Default settings correction < 10% (quality of defaults) 13. Search results quality > 80% (for top 100 most popular queries) 14. Service desk inquiries < 35/week (poor design → more inquiries) 15. Form input accuracy ≈ 100% (user input in forms) 16. Time to final price < 45s (for eCommerce) 17. Password recovery frequency < 5% per user (for auth) 18. Fake email frequency < 2% (for email newsletters) 19. First contact resolution < 85% (quality of service desk replies) 20. “Turn-around” score < 1 week (frustrated users → happy users) 21. Environmental impact < 0.3g/page request (sustainability) 22. Frustration score < 5% (AUS + SUS/SUPR-Q + Lighthouse) 23. System Usability Scale > 75 (overall usability) 24. Accessible Usability Scale (AUS) > 75 (accessibility) 25. Core Web Vitals ≈ 100% (performance) Each team works with 3–4 local design KPIs that reflects the impact of their work, and 3–4 global design KPIs mapped against touchpoints in a customer journey. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [more in the comments ↓] #ux #metrics

AI products do more than introduce a new interface pattern. They reshape the interaction itself. In traditional systems, people gradually learn the rules, form expectations, and usually become more efficient with repeated use. AI changes that rhythm. A system may feel highly capable while still being inconsistent, opaque, overly persuasive, or confidently wrong in ways users do not catch right away. For that reason, evaluating AI through the same lens we use for ordinary digital products leaves out too much. In many teams, evaluation still centers on familiar questions. Is the system usable? Do people enjoy it? Can they complete the task? Those questions still matter, but they do not capture the full experience. An AI feature can feel polished and still lead users toward overtrust. An assistant can seem fast and impressive while actually increasing effort because people have to verify outputs, manage uncertainty, and fix errors. A product can feel smooth on the surface while still producing unfair outcomes or nudging people toward poor decisions. Human AI evaluation needs a wider and more grounded scope. Usability remains essential because a confusing interface can undermine everything else. But beyond that, teams need to examine whether the system is truly useful, whether it improves judgment, whether people understand how it behaves, and whether trust is appropriately calibrated. The goal is not simply to make users feel confident. The goal is to help them rely on the system when it is appropriate and question it when needed. Mental models, perceived control, and collaboration also deserve much more attention. Many AI systems are framed as assistants, copilots, or partners, which means the relationship between person and system becomes part of the user experience. Researchers need to ask whether the AI strengthens human judgment or gradually displaces it, whether it reduces effort or merely shifts effort into hidden checking and correction work. In many AI products, these dynamics are central to the experience rather than secondary concerns. The more difficult side of evaluation matters just as much. Fairness, safety, accountability, and recovery from failure cannot be treated as edge cases. AI systems will fail at times. What matters is whether users can detect those failures, respond effectively, and recover without losing orientation, performance, or trust. A strong AI experience is not defined by the absence of mistakes. It is defined by how well the system supports people when mistakes happen. That is why AI evaluation should extend well beyond usability and satisfaction. It should also address usefulness, trust calibration, explainability, agency, cognitive burden, fairness, safety, resilience, and emotional fit.

How to Use Gemini 3 Pro to Analyze Your UI Design Here’s a simple, practical workflow I use: Every designer has faced this: The UI looks clean. The screens feel polished. But something still doesn’t convert. Users hesitate. Flows feel confusing. Feedback sounds vague. That’s where AI-assisted UI analysis helps. Here’s how to do it properly: Step 1: Start With Real Screens Not Dribbble shots. Not random concepts. Use real product screens. Focus on actual user flows. - Login - Onboarding - Checkout - Core actions. Garbage input always gives garbage feedback. Step 2: Export With Intention Export screens as images. PNG or JPG works best. Keep labels visible. Avoid half-finished versions. Clarity matters more than quantity. Step 3: Clean the Noise Before uploading, review everything. Remove unused variations. Drop old experiments. Keep only final, connected flows. Think like a reviewer, not a designer. Step 4: Ask the Right Questions Don’t just upload and hope. Be specific: “What usability issues do you see?” “Where might users get confused?” “What feels heavy or unclear?” Good prompts unlock good insights. Step 5: Spot Patterns, Not Opinions One comment can be ignored. Repeated feedback is a signal. Look for: - Friction points. - Confusion. - Hierarchy problems. Patterns matter more than details. Step 6: Structure the Feedback Ask for summaries. Ask for priority-based issues. Ask for simple tables or lists. This turns raw feedback into action. Step 7: Design With Confidence Now you’re not guessing. You’re fixing real problems. Improve clarity. Refine flows. Strengthen hierarchy. That’s how AI becomes a design partner, not a shortcut. If you’re still reviewing your UI only by gut feeling, you’re leaving clarity on the table. Save this. Try it on your next screen. And tell me what surprised you most. ♻️ Repost to help your network. Follow Rasel Ahmed, Co-Founder of Musemind and Mentor Lane.

For years, UX and HCI work centered around performance metrics, clicks, errors, time on task. Useful, yes, but they only skim the surface. They tell us what people did, not why they did it or how they felt. And emotion shapes everything. Stress can make a simple interface feel confusing. A small delay feels worse when someone is anxious. Confidence makes complex flows feel easy, while frustration makes even the simplest task feel impossible. When we measure emotion alongside behavior and perception, we finally see how people actually experience technology. Getting that full picture means looking at multiple layers at once. We pay attention to what users say they feel, the small facial cues they show without realizing it, the way their bodies react automatically, and the subtle behavioral patterns hidden in how they move, scan and navigate. Subjective ratings tell us how people frame their own experience. Facial patterns reveal early signs of confusion or relief. Physiological signals like arousal, cognitive load and micro-shifts in attention give us moment-by-moment emotional truth. And interaction traces, cursor paths, gaze shifts, hesitation, scrolling, show emotional friction at scale. In fact, the real insight comes from merging these signals, not treating them separately. Together, they create an emotional narrative that explains breakdowns, hesitation, engagement and delight far better than task metrics alone. Without emotional data, we miss early frustration, hidden cognitive load and the reason two users can have the same performance outcome but completely different experiences. And different projects call for different emotional toolkits. Sometimes self-reports and interaction logs are all you need. Other times you need deeper physiological measures or more detailed behavioral observation. Emotion is highly context dependent, so our methods have to be flexible. If you want to dive deeper into the full article and methods, you can read more here: https://lnkd.in/emeh_SGf

Recent debate in the world of design finds ourselves confused between design as personal choice or the product of a well calculated UX strategy. It’s tempting to lean on aesthetics that feel “right” or ideas that align with personal taste. But when designing for business, it's crucial to look beyond what we like and focus on what works. Here’s why aligning design choices with KPIs and UX metrics drives results. Imagine designing a user interface based purely on color schemes we love or animations that feel fun. While personal style brings creativity to the table, it often lacks a strategic focus. For example, a designer might feel that an intricate navigation system looks sleek. But if UX metrics reveal high abandonment rates at navigation points, that “cool” design is clearly not resonating with users. Here, usability should trump aesthetics every time. KPIs (Key Performance Indicators) and UX metrics – like conversion rates, task success rates, or time-on-task – are not just data points. They’re our users’ voices, telling us what they need and expect. When a design aligns with these metrics, it speaks directly to user behavior and business objectives. This is where real value is created. Let’s prioritize intuitive, data-driven design that serves the user and meets business goals. Personal taste may spark inspiration, but data is what drives sustainable impact. Design that’s user-centered, measurable, and flexible isn’t just visually appealing; it’s strategically valuable. So, next time you face a design decision, ask yourself: Is this about personal taste, or does it align with key metrics? The answer might just change the way you design. 💡 #DesignThinking #UserExperience #UXMetrics #KPIs #ProductDesign

AI changes how we measure UX. We’ve been thinking and iterating on how we track user experiences with AI. In our open Glare framework, we use a mix of attitudinal, behavioral, and performance metrics. AI tools open the door to customizing metrics based on how people use each experience. I’d love to hear who else is exploring this. To measure UX in AI tools, it helps to follow the user journey and match the right metrics to each step. Here's a simple way to break it down: 1. Before using the tool Start by understanding what users expect and how confident they feel. This gives you a sense of their goals and trust levels. 2. While prompting  Track how easily users explain what they want. Look at how much effort it takes and whether the first result is useful. 3. While refining the output Measure how smoothly users improve or adjust the results. Count retries, check how well they understand the output, and watch for moments when the tool really surprises or delights them. 4. After seeing the results Check if the result is actually helpful. Time-to-value and satisfaction ratings show whether the tool delivered on its promise. 5. After the session ends See what users do next. Do they leave, return, or keep using it? This helps you understand the lasting value of the experience. We need sharper ways to measure how people use AI. Clicks can’t tell the whole story. But getting this data is not easy. What matters is whether the experience builds trust, sparks creativity, and delivers something users feel good about. These are the signals that show us if the tool is working, not just technically, but emotionally and practically. How are you thinking about this? #productdesign #uxmetrics #productdiscovery #uxresearch

A proposed qualitative evaluation framework for Generative AI writing tools: This post is my first draft of an evaluation framework for assessing generative AI tools (e.g. Claude, ChatGPT, Gemini). It's something I’ve been working on with Ryan Low — originally in the interest of selecting the best option for Rotational. At some point we realized sharing these ideas might help us and others out there trying to pick the best AI solution for your company's writing needs. We want to be clear that this is not another LLM benchmarking tool. It's not about picking the solution that can count the r's in strawberry or repeatably do long division. This is more about the everyday human experience of using AI tools for our jobs, doing the kinds of things we do all day solving our customers' problems 🙂. We're trying to zoom in on things that directly impact our productivity, efficiency, and creativity. Do these resonate with anyone else out there? Has anyone else tried to do something like this? What other things would you add? Proposed Qualitative Evaluation Criteria 1 - Trust and Accuracy Do I trust it? How often does it say things that I know to be incorrect?  Do I feel safe? Do I understand how my data is being used when I interact with it? 2 - Autonomous Capabilities How much work will it do on my behalf? What kinds of research and summarization tasks will it do for me? Will it research candidates for me and draft targeted emails? Will it read documents from our corporate document drive and use the content to help us develop proposals? Will it review a technical paper, provided a URL? 3 - Context Management and Continuity How well does the tool maintain our conversation context? Not to sound silly, but does the tool remember me? Is it caching stuff? Is there a way for me to upload information about myself into the user interface so that I don’t have to continually reintroduce myself? Does it offer a way to group our conversations by project or my train of thought? Does it remember our past conversations? How far back? Can I get it to understand time from my perspective? 4 - User Experience Does the user interface feel intuitive? 5 - Images How does it do with images? Is it good at creating the kind of images that I need? Can the images it generates be used as-is or do they require modification? 6 - Integrations Does it integrate with our other tools (e.g. for project management, for video conferences, for storing documents, for sales, etc)? 7 - Trajectory Is it getting better? Does the tool seem to be improving based on community feedback? Am I getting better at using it?

You've heard me say that UX should be invisible, that the user should use the design seamlessly, without drawing attention to itself. It should enable users to interact with the system naturally, without unnecessary interruptions or confusion. Here's how UX could be invisible: - Align with User Mental Models: The design should match how users think and expect things to work. This means understanding users deeply—how they approach tasks, their mental shortcuts, and their expectations. When the design aligns with these mental models, users don’t have to pause and learn; they just act, and the interface works as anticipated. - Streamline Tasks and Remove Clutter: An invisible UX simplifies tasks by removing unnecessary steps and presenting only what is essential at each stage. Every element on the interface has a purpose directly tied to the user's goal. By stripping away anything extraneous, users can complete their tasks without distraction. - Guide Users Subtly, Not Forcefully: Instead of overt instructions or heavy-handed guidance, the interface should provide subtle cues that guide users gently. This could be through visual hierarchy, natural language, or affordances that hint at what actions are possible. Users should feel in control and empowered rather than managed or restricted by the design. - Error Prevention and Recovery: The design should anticipate potential user errors and prevent them before they occur. If errors do happen, the system should offer simple, immediate ways to correct them without penalty or frustration. - Consistency in Interaction Patterns: Consistent design patterns help users build a reliable mental map of how to interact with the system. Use familiar conventions so users feel comfortable and confident. Consistency reduces the learning curve and makes the interaction feel second nature, contributing to the sense of an invisible UX. - Proactive Support Without Interference: Interfaces could offer proactive help—like suggestions, auto-completions, or predictive inputs—exactly when needed, but without overwhelming the user. The support should feel like an enhancement rather than an interruption. - Design for Flow: Design for flow, where users are fully engaged and can move through tasks without disruption. Remove points of friction and create smooth transitions between different parts of the task, allowing users to maintain their momentum and focus. - Functional Simplicity: Invisible UX focuses on the core functions that directly contribute to user goals, avoiding unnecessary features or complexities that might confuse or slow down the user. Good UX is not about showcasing every possible feature but about prioritizing what’s truly necessary for the user’s success. In summary, create an experience that is so aligned with the user's needs, expectations, and behaviors that it becomes an almost subconscious interaction. The user should achieve what they set out to do with minimal thought about the interface.

Design validation testing and human factors Validation Human factors (HF) validation and user interface (UI) design validation are both performed at the end of a product’s development to ensure that the product is suitable for its intended use, users, and use environments, but each with a slightly different scope. Whereas HF validation testing is conducted to generate data related to whether users can interact with the product safely and effectively, UI design validation is conducted to yield evidence that the product meets users’ needs.   Noting the similarities between HF validation and user interface design validation, manufacturers might wonder how they can combine these activities. Incorporating design validation activities into human factors validation testing can be an excellent use of time and resources. This is especially true when the combined human factors and design validation test sessions are short enough that participants are not fatigued by the session’s end, enabling them to participate fully and generate robust data. Furthermore, combining these activities is useful from a recruiting perspective, noting you can recruit one set of participants for the combined session rather than recruiting one set of participants for each activity.  Consider the following best practices to ensure you are integrating user interface design validation elements into HF validation testing is a smooth and productive endeavor:  Understand the scope of HF validation and UI design validation activities. For the HF validation test, you will have participants complete use scenarios and knowledge tasks encompassing all critical tasks, and many of these activities will likely be important for user interface design validation as well. Ensure you have a clear understanding of what activities must be performed for the HF validation as well as the UI design validation such that you can determine what additional activities should be added for UI design validation. Furthermore, consider if you need to collect additional data from the HF validation test activities beyond what is traditionally planned (e.g., dominant hand used, glove size, task time).   Complete HF validation test activities before user interface design validation questions. Noting the necessary rigor in an HF validation test to avoid bias and represent a realistic interaction, you should not ask any UI design validation questions until you have completed all HF validation test activities. Completing the HF validation test activities would include completing any debrief on use scenarios and knowledge task performance, as well as any further subjective feedback questions. Furthermore, if there are additional hands-on activities required to validate the UI design elements that were not already encompassed within the HF validation test, you should also wait until completing the HF validation test activities to proceed with these UI design validation activities.  

10 Heuristic Evaluation Principles Every Designer Should Know 💡 If a UX Audit is your product’s health check-up, then Heuristic Evaluation is the diagnosis tool that tells you why users feel the way they do. It’s a method to test your product against tried-and-true usability principles by Jakob Nielsen timeless guidelines that make digital experiences feel effortless and human. 1. Visibility of System Status Users should always know what’s going on. The system must keep them informed through feedback within a reasonable time. #Example: Loading spinners, progress bars, “Saved successfully” messages. 2. Match Between System and the Real World Use familiar language, concepts, and real-world logic instead of system-oriented terms. #Example: A trash can icon for “delete,” or a shopping cart icon for “add to cart.” 3. User Control and Freedom Users should be able to undo or redo actions easily and exit unwanted states. #Example: “Undo” buttons, “Cancel” options, or a visible back navigation. 4. Consistency and Standards Follow design conventions so users don’t have to guess. Keep similar elements behaving consistently. #Example: Buttons that look alike should perform similar actions across screens. 5. Error Prevention Design proactively to avoid mistakes before they happen. #Example: Confirmation prompts (“Are you sure you want to delete this?”) or disabled buttons until all fields are filled. 6. Recognition Rather Than Recall Reduce memory load by showing options instead of making users remember information. #Example: Autofill suggestions, dropdown lists, or visible menu options. 7. Flexibility and Efficiency of Use Allow both beginners and experts to use the product efficiently. #Example: Keyboard shortcuts for advanced users, while still keeping intuitive navigation for new ones. 8. Aesthetic and Minimalist Design Keep interfaces clean and focused. Avoid unnecessary information that competes with key tasks. #Example: White space, simple typography, and only essential elements on screen. 9. Help Users Recognize, Diagnose, and Recover from Errors When errors occur, explain them clearly and guide users on how to fix them. #Example: “Incorrect password. Try again or reset it here.” instead of “Error 401.” 10. Help and Documentation Provide accessible help when users need support, even if the system is simple. #Example: Searchable FAQs, tooltips, or guided tours for new users. These principles are not just UX rules they’re the foundation of empathetic design that makes technology feel human. 💡 Let me know your thoughts on this! 😊 #heuristicevaluation #uxaudit #uxdesign #uiux #userexperience #usability #designthinking #uxresearch #uxprinciples #digitalproductdesign #interactiondesign #uxstrategy #productdesign #uxcommunity #uxinsights #designforusers #uxbestpractices #uxlearning #usercentricdesign #uxleadership