User Interface Adaptation Strategies

🌎 Designing Cross-Cultural And Multi-Lingual UX. Guidelines on how to stress test our designs, how to define a localization strategy and how to deal with currencies, dates, word order, pluralization, colors and gender pronouns. ⦿ Translation: “We adapt our message to resonate in other markets”. ⦿ Localization: “We adapt user experience to local expectations”. ⦿ Internationalization: “We adapt our codebase to work in other markets”. ✅ English-language users make up about 26% of users. ✅ Top written languages: Chinese, Spanish, Arabic, Portuguese. ✅ Most users prefer content in their native language(s). ✅ French texts are on average 20% longer than English ones. ✅ Japanese texts are on average 30–60% shorter. 🚫 Flags aren’t languages: avoid them for language selection. 🚫 Language direction ≠ design direction (“F” vs. Zig-Zag pattern). 🚫 Not everybody has first/middle names: “Full name” is better. ✅ Always reserve at least 30% room for longer translations. ✅ Stress test your UI for translation with pseudolocalization. ✅ Plan for line wrap, truncation, very short and very long labels. ✅ Adjust numbers, dates, times, formats, units, addresses. ✅ Adjust currency, spelling, input masks, placeholders. ✅ Always conduct UX research with local users. When localizing an interface, we need to work beyond translation. We need to be respectful of cultural differences. E.g. in Arabic we would often need to increase the spacing between lines. For Chinese market, we need to increase the density of information. German sites require a vast amount of detail to communicate that a topic is well-thought-out. Stress test your design. Avoid assumptions. Work with local content designers. Spend time in the country to better understand the market. Have local help on the ground. And test repeatedly with local users as an ongoing part of the design process. You’ll be surprised by some findings, but you’ll also learn to adapt and scale to be effective — whatever market is going to come up next. Useful resources: UX Design Across Different Cultures, by Jenny Shen https://lnkd.in/eNiyVqiH UX Localization Handbook, by Phrase https://lnkd.in/eKN7usSA A Complete Guide To UX Localization, by Michal Kessel Shitrit 🎗️ https://lnkd.in/eaQJt-bU Designing Multi-Lingual UX, by yours truly https://lnkd.in/eR3GnwXQ Flags Are Not Languages, by James Offer https://lnkd.in/eaySNFGa IBM Globalization Checklists https://lnkd.in/ewNzysqv Books: ⦿ Cross-Cultural Design (https://lnkd.in/e8KswErf) by Senongo Akpem ⦿ The Culture Map (https://lnkd.in/edfyMqhN) by Erin Meyer ⦿ UX Writing & Microcopy (https://lnkd.in/e_ZFu374) by Kinneret Yifrah

How do you wire interface to intent? Like how do you *actually* do it? I talk a lot about designing interfaces that adapt in real time to user intent and context, and it’s easier than you might think. Here’s a primer for designers. I made this video to show how to quickly prototype a system that chooses the right design pattern to display for the specific context. This kind of exploration has become an essential part of our early-stage work in design projects at Big Medium. We sketch behavior design in words. I also wrote it up to explain what it does and how this approach fits into the design process (link in the comments). The gist: it used to be really, really hard for systems to determine user intent from natural language or other cues. But now… LLMs just get it. And if you give them a clear, constrained system to match that intent to specific design patterns, they’re really good at making the connection. This lets you deliver radically adaptive experiences: interfaces that change content, structure, style, or behavior—sometimes all at once—to provide the right experience for the moment. In this context, the LLM’s job shifts from direct chat to mediating simple design decisions. It acts less as a conversational partner than as a stand-in production designer assembling building-block UI elements or adjusting interface settings. As the designer, your role shifts to creative director, defining the interaction language and rules. It’s design system work for real-time production. This also means that designers become important contributors to the system prompt, because it’s where the system’s behavior design happens. As the example shows, these prompts don’t have to be rocket science; they’re plain-language instructions telling the system how and why to use certain interface conventions. That’s the kind of thinking and explanation that designers excel at: describing what the interface does and why. Only now you’re describing this logic to the system itself. It’s also what LLMs excel at. This approach uses LLMs for what they do best (intent, manner, and syntax) and sidesteps where they’re wobbly (facts and complex reasoning). It’s safe and reliable with vanishingly small hallucination rates. Give it a try! The article linked in comments includes a link to try out the system prototyped in the video -- with lots of tips and simple design patterns for how you can build this into your own practice and products.

One of the constant challenges in UI/UX design is creating websites that serve diverse user needs effectively. While development and research teams often aim for universal accessibility, end users arrive with vastly different objectives. Consider Apple's website - visitors might need MacOS update information, iPhone purchasing, technical support, laptop upgrades, or countless other Apple-related services. Yet their homepage prominently features only their latest phone model at the top. This one-size-fits-all approach, while efficient for high-traffic priorities, can now be fundamentally reimagined through AI-driven personalization. Large Language Models enable us to aggregate visitor context and dynamically generate user interfaces that adapt to individual needs in real-time. This shift from static layouts to Generative UI (GenUI) demonstrates a significant change in how we approach web experiences. To explore this concept, I built a demonstration using GenUI techniques - specifically implementing an LLM model to generate complete user interfaces based on user needs and context in a laptop purchasing e-commerce setting. By combining existing user information with guided conversation, the LLM is able to dynamically generate and modify webpage content to precisely match a user’s individual preferences. Rather than navigating through generic product pages, users experience interfaces explicitly tailored to their requirements at that exact moment. The technical implementation leverages several key components: 1. Real-time UI generation based on conversational context 2. Dynamic content adaptation using visitor data 3. Integration patterns that maintain responsive performance This approach fundamentally disrupts traditional UI/UX methodologies, where interfaces are often designed once for many users. Instead, GenUI enables interfaces that are generated uniquely for each user, each time. To watch how GenUI is reshaping web experiences, learn the specific techniques I used, and see this demo in action check out my latest video: https://lnkd.in/evXBq9wc

agents that learn your workflows > agents that relearn you every day. I’m sharing a standout research report: Log2Plan, an adaptive GUI automation framework powered by task mining. It learns from real interaction logs, builds a reusable plan, and then adapts each step to the live screen. Think: global plan + local grounding, so agents get more reliable the longer you use them. ↳ Why this matters for UX/UI: ➤ Personalization without hero prompts, the system internalizes how you work (file paths, naming, exception paths). ➤ Recoverable runs, step-level checks and quick human-assist beats brittle macro replays. ➤ Transparent actions, structured plans you can read, audit, and improve. ➤ Resilience to UI drift, intent stays stable even when buttons and layouts move. ↳ What’s actually new here: ➤ Task mining turns messy click/keystroke logs into reusable “Task Groups” (ENV / ACT / Title / Description). ➤ Retrieval-augmented planning pulls the right pieces for a new goal, then the local planner fits them to the current screen. ➤ A clear separation of plan vs. interaction that reduces token bloat and flaky screenshot reasoning. ↳ Try this week (operator’s cut): ➤ Pick one high-volume desktop flow (e.g., monthly report collation). ➤ Curate 2–3 clean traces into “Task Groups.” ➤ Define success metrics (success rate, sub-task completion, time per task, assist rate). ➤ Add human-assist checkpoints for sensitive steps and ship a small pilot. Follow for more UX/UI & AI implementations. Re-share with your network.

Forget what you know about UI. (here comes outcome-oriented UI) A new paradigm is emerging in UI design. Now where user goals trump traditional UI elements. Thanks to AI and generative UI principles. Outcome-oriented design will revolutionize how we create digital experiences. 5 ways to implement Outcome-oriented UI design: 1. GOAL-BASED NAVIGATION: Ditch traditional menus for AI-powered, goal-oriented navigation. Example: A banking app that presents options based on the user's financial goals (e.g., "Save for a house," "Reduce debt") rather than generic account categories. 2. ADAPTIVE WORKFLOWS: Create interfaces that morph to match the user's current objective. Example: A video editing tool that simplifies or expands its interface based on whether the user is making a quick social media clip or a professional-grade film. 3. PREDICTIVE TASK COMPLETION: Leverage AI to anticipate and streamline user tasks. Example: A project management platform that automatically generates and populates task lists based on team goals, past projects, and current deadlines. 4. CONTEXTUAL INFORMATION HIERARCHY: Dynamically adjust info prominence based on user context and goals. Example: An e-commerce site that prioritizes different product descriptions (e.g., sustainability, price, delivery time) based on each user's shopping priorities and behavior. 5. INTELLIGENT FORM OPTIMIZATION: Design forms that adapt to user goals and known information. Example: A travel booking system that only asks for relevant information based on the type of trip (business vs. leisure) and automatically fills in known preferences. ................................................................................. Outcome-oriented UI design focuses on what users want to achieve, not how they navigate an interface. Designers embracing this approach will create more intuitive, efficient, and personalized digital experiences. The future of UI isn't about buttons and menus – it's about understanding and facilitating user goals.