AI Enhancements For Interactive User Experiences

Human conversation is interactive. As others speak you are thinking about what they are saying and identifying the best thread to continue the dialogue. Current LLMs wait for their interlocutor. Getting AI to think during interaction instead of only when prompted can generate more intuitive and engaging Humans + AI interaction and collaboration. Here are some of the key ideas in the paper "Interacting with Thoughtful AI" from a team at UCLA, including some interesting prototypes. 🧠 AI that continuously thinks enhances interaction. Unlike traditional AI, which waits for user input before responding, Thoughtful AI autonomously generates, refines, and shares its thought process during interactions. This enables real-time cognitive alignment, making AI feel more proactive and collaborative rather than just reactive. 🔄 Moving from turn-based to full-duplex AI. Traditional AI follows a rigid turn-taking model: users ask a question, AI responds, then it idles. Thoughtful AI introduces a full-duplex process where AI continuously thinks alongside the user, anticipating needs and evolving its responses dynamically. This shift allows AI to be more adaptive and context-aware. 🚀 AI can initiate actions, not just react. Instead of waiting for prompts, Thoughtful AI has an intrinsic drive to take initiative. It can anticipate user needs, generate ideas independently, and contribute proactively—similar to a human brainstorming partner. This makes AI more useful in tasks requiring ongoing creativity and planning. 🎨 A shared cognitive space between AI and users. Rather than isolated question-answer cycles, Thoughtful AI fosters a collaborative environment where AI and users iteratively build on each other’s ideas. This can manifest as interactive thought previews, real-time updates, or AI-generated annotations in digital workspaces. 💬 Example: Conversational AI with "inner thoughts." A prototype called Inner Thoughts lets AI internally generate and evaluate potential contributions before speaking. Instead of blindly responding, it decides when to engage based on conversational relevance, making AI interactions feel more natural and meaningful. 📝 Example: Interactive AI-generated thoughts. Another project, Interactive Thoughts, allows users to see and refine AI’s reasoning in real-time before a final response is given. This approach reduces miscommunication, enhances trust, and makes AI outputs more useful by aligning them with user intent earlier in the process. 🔮 A shift in human-AI collaboration. If AI continuously thinks and shares thoughts, it may reshape how humans approach problem-solving, creativity, and decision-making. Thoughtful AI could become a cognitive partner, rather than just an information provider, changing the way people work and interact with machines. More from the edge of Humans + AI collaboration and potential coming.

Most #AI interfaces still assume users are prompt whisperers. But let’s be honest: In the real world, people don’t want to “#tune” prompts. They want to get things done — precisely, quickly, reliably. That’s why I found Microsoft Research’s latest work on Promptions worth highlighting. It’s a lightweight framework that wraps dynamic UI controls around prompts, so users can steer generative AI without starting from scratch every time. What stood out 👇 1️⃣ Prompting is becoming interactive Users get buttons, sliders, toggles — not just a blinking cursor. 2️⃣ Controls evolve with context As the conversation flows, so do the options — making interaction feel intelligent, not rigid. 3️⃣ No more prompt fatigue Users in early studies got more accurate outputs with less mental strain. 4️⃣ Works across models Promptions is model-agnostic. Use it with GPT, local LLMs, or internal enterprise agents. 5️⃣ Developer-friendly It’s open-source (MIT license) and easily pluggable into existing agent UIs. 6️⃣ Built for productivity Whether you're generating copy, answering support queries, or analyzing data — it guides users to outcomes faster. 7️⃣ Reimagines prompting as design This moves the interface from words-as-code to controls-as-clarity. Bottom line: Promptions shifts the paradigm — from prompt engineering to prompt experience design. And that opens doors for scalable, low-friction AI use across the enterprise. 🔗 Read the full research blog here: https://lnkd.in/g4KMRTWu #UXForAI #PromptDesign #ProductivityAI #AgentUX #AIInteraction #MicrosoftResearch

🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike” AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👍 https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👍 https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👍 https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design

Google DeepMind’s Genie 3 moves generative AI from images to fully interactive worlds.   With a single text prompt, Genie 3 can create a 3D environment that can be explored in real time, complete with physics, object interactions, and dynamic events.    Earlier versions, like Genie 2, allowed only 10–20 seconds of interaction before resetting. Genie 3 extends this to several minutes, with the AI remembering objects for about a minute, meaning if you look away from a chalkboard or wall and return, it’s still there. Visuals now run at 720p and 24fps, giving the experience a greater sense of stability.   This capability opens new possibilities for: ►Transforming photographs or videos into playable scenes ► Building training environments for robotics or autonomous systems ► Rapidly prototyping virtual worlds for design and research   It’s the shift from observing to inhabiting, a move towards AI that doesn’t just represent reality, but constructs and simulates it. 📹Video: Google DeepMind #Google #Artificialintelligence

From GenAI to GenUI We’re witnessing a shift as significant as the leap from MS-DOS to graphical user interfaces. The AI era marks our latest upgrade in how we interact with technology. For decades, we designed for workflows and specific actions. Everything was deterministic. Behind every interface sat a flowchart, with logic carefully coded. The backend made decisions, and the frontend rendered them. This model worked because we could predict every path a user might take. With agents, this paradigm breaks down. Text alone isn’t sufficient anymore. Chat works for conversation, but interaction demands something more. We need to engage with agents, not just talk to them. Reasoning state and intent become critical factors in the exchange. LLMs can now generate UI, and this capability feels like natural progression. Model Context Protocol enables mini-apps to emerge on the fly, no longer bound by deterministic rules. This opens the door to genuine hyper-personalization. We’ve moved from designing screens to designing for outcomes. Agents now dynamically assemble workflows based on intent, available data, and accessible tools. The fact that agents can create interfaces without traditional designers and developers is revolutionary. We can finally shift from UI-centric thinking to truly user-centered experience design. This fundamentally transforms the designer’s role. We’re no longer pixel pushers or interface assemblers. The work of arranging buttons, spacing elements, and crafting individual screens can now be handled by agents. Instead, designers become architects of experience, defining the principles, guardrails, and intent that shape how agents respond. We set the boundaries of possibility, orchestrate the logic of interaction, and ensure coherence across dynamic, personalized experiences. Our canvas expands from static screens to adaptive systems. We design the intelligence behind the interface, the relationships between user needs and agent capabilities, the quality standards that govern generated UIs. We curate outcomes rather than outputs. The ability to adapt, reorganize, and respond to both user intent and application context is transformative. With reasoning and action combined, agents can generate dynamic artifacts that enable interaction, not merely conversation. What a time to be alive as a designer! 🫶 #ai

How proactive AI will change UX - 📆 schedule ChatGPT requests! OpenAI has introduced a new task scheduling feature for ChatGPT. This means you can now ask ChatGPT to handle tasks at a future time — like sending you a weekly global news update, recommending a daily personalized workout, or setting reminders for important events. 💡 Why is this interesting from a UX perspective? This shift is a step toward proactive AI — moving from reactive systems (waiting for user input) to anticipatory, context-aware experiences that help users save mental energy and stay on top of their routines. Let’s break it down from a real-life use case - creating daily recipes: I currently eat sugar-free, gluten-free (because I am celiac), and generally low-carb and like to let ChatGPT create recipes for me. I don’t want a fixed meal plan, but I do need flexible, personalized recipe suggestions that fit my nutrition goals. Ideally, I’d want ChatGPT to  → suggest automatically 3-4 recipes daily around 3 PM → send them to me  → and based on my choice adjust future suggestions for the next days based on what I’ve already eaten that week (for balanced nutrients). With the new task feature, this kind of personalized experience could become much much more seamless. I wouldn't need to ask repeatedly — the assistant would learn my preferences over time and adapt its suggestions accordingly. 🎯 What can we learn from this in AI-UX design? 1️⃣ From static interactions to dynamic experiences: We often design AI tools that rely on users asking for something. But this update shows the value of continuous, evolving interactions. Users shouldn’t need to start from scratch every time — systems can proactively adjust to their needs and context. 2️⃣ Mental models of AI assistants: For users to trust AI routines, they need to understand what the assistant will do and when. It’s about designing predictability and transparency in a way that still allows for flexibility and spontaneity. 3️⃣ Proactive ≠ intrusive: There’s a fine balance between helpful and annoying. The best AI interactions feel like a supportive partner — offering assistance at the right time, based on context and past behavior, without overwhelming users with irrelevant notifications. In AI-UX, we’re increasingly designing for systems that adapt and evolve with the user.  This new feature is a great example of how AI can shift might be able rom a passive tool to an active assistant — can’t wait to try it. How do you see proactive AI changing the way we design user experiences? Would love to hear your thoughts! 👀

The UX of Agentic Graph Systems: Beyond Chat Interfaces 🌓 Agentic graph systems represent a significant evolution in AI architecture, combining the structured knowledge representation of graphs with the generative capabilities of large language models to create systems capable of autonomous planning and execution. As AI systems evolve from simple pattern-matchers to agentic systems with continuous observe-think-act cycles, our user interfaces must evolve accordingly. Current chat-based interfaces, while familiar, prove fundamentally limiting for the complex workflows that true agentic systems enable. Traditional chat interfaces impose substantial constraints when working with AI agents on complex tasks. The sequential, text-based format creates a "linear conversation flow" that makes it difficult to represent non-linear workflows with dependencies, branches, or parallel processes. These interfaces struggle with visualization capabilities, making it challenging to display complex data structures or relationships. They also feature inefficient error correction mechanisms—when an AI makes a mistake, fixing it through chat creates verbose, confusing conversation histories that increase cognitive load for users. Vector-based RAG approaches, while prevalent, flatten complex relationships and lose crucial connections between entities. Graph-based systems address fundamental limitations by embedding structural knowledge representation within neural processing frameworks. This architecture enables the capture of complex relational networks that define enterprise knowledge—preserving not just what information exists, but how it connects across organizational boundaries. Visualizing these relationships becomes essential for human understanding and interaction with the system. Several compelling alternative UI paradigms that offer significant advantages over traditional chat: Agent Dashboards: Command centers showing AI reasoning and confidence levels, enabling "observable autonomy" where users can monitor and influence AI decision-making without constant micromanagement. Workflow Graphs: Visual, editable task maps that transform linear task lists into spatial, interactive mind maps where tasks, decisions, and actions are visually represented and modifiable. Editable AI Notebooks: Structured, persistent documents that both AI and humans can continuously update and reference, creating shared, actionable memory that preserves context. Multimodal UIs: Interaction beyond text, incorporating drag-and-drop interfaces, voice commands, and spatial representations that reduce friction in human-AI collaboration. Interactive visualization transforms abstract data relationships into intuitive visual mappings, enabling developers and users to clearly understand connections. Continuez en commentaire :

𝗜𝗻𝘁𝗲𝗻𝘁 𝗯𝘆 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: Designing the AI User Experience AI is not just a better chat box. It changes the user’s role from operator to supervisor, which forces UX to move from command-based interaction toward intent-based delegation, new usability metrics, orchestration layers, calibrated friction, and ultimately exploration-based interaction to clarify the user’s needs. As software shifts from apps to AI agents, mature intent-based systems will settle into a triple-layered design model: 🎯 𝗜𝗻𝘁𝗲𝗻𝘁 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: Where users state outcomes. Context-aware and multimodal, this layer increasingly infers implicit intent from ambient signals: drafting the prompt so users just confirm. 🔍 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: The negotiation layer. Agents reveal plans, seek consent, and provide post-action receipts. In enterprises, it resolves collaborative intent: flagging conflicts, enforcing policies, and showing who's affected before execution. 🖐️ 𝗗𝗶𝗿𝗲𝗰𝘁 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: The GUI lives on as a fallback for inspection, correction, and override. But users now manipulate plans, not raw controls: retaining hands-on agency at a higher level of abstraction. My full article 👉 https://lnkd.in/grRVAhTe

Why Traditional Call Centers Are Transitioning to AI-First Support Customer expectations have evolved. They now demand instant responses, round-the-clock availability, and consistent experiences across every channel. Traditional call-center models cannot meet these requirements at scale - AI can. Key Drivers Behind the Shift Rising Customer Expectations Customers prefer real-time support over waiting on hold. AI enables instant, accurate responses across chat, voice, and digital channels. Increasing Operational Costs Recruitment, training, and agent attrition create ongoing cost pressures. AI manages repetitive queries at near-zero marginal cost, allowing organizations to scale efficiently. High Volume of Repetitive Queries Up to 70% of support requests are routine (order updates, resets, FAQs). AI resolves these immediately, allowing human agents to focus on complex, high-value interactions. 24×7 Availability Is Now Essential While human agents work in shifts, customers expect continuous support. AI ensures uninterrupted service - even during nights, weekends, and peak times. Faster Resolution, Better CX AI can instantly search knowledge bases, suggest responses, and predict next issues, reducing handling time and minimizing customer frustration. Seamless Omnichannel Experience AI connects conversations across chat, email, voice, WhatsApp, and in-app channels, ensuring context moves with the customer. AI Enhances Human Capability AI is not replacing human agents - it is augmenting them. AI handles scale and speed. Humans handle empathy and complex decision-making. The result: higher customer satisfaction and more empowered support teams.

AI systems are getting smarter about admitting what they can't do. Ask ChatGPT to calculate your 401k projections or estimate enterprise software ROI, and instead of attempting the math, it increasingly says "try this calculator" and links to interactive tools. This behavioral shift reveals a massive opportunity that most companies are missing entirely. We’re starting to see AI systems refer users to calculators or interactive tools in scenarios where static answers fall short. We noticed this pattern while analyzing citation behavior for uSERP clients. AI systems recognize their computational limitations and actively seek functional solutions for calculation-heavy queries. Here’s why this matters: Users asking calculation questions have high purchase intent. They're not just researching — they're actively evaluating solutions with their specific numbers. Here’s how to leverage this interactive tool opportunity: • Target calculation-heavy queries where users need personalized numbers based on their specific inputs. ROI calculations, pricing tools, savings estimators — any query where generic answers won't work. • Build lightweight, functional tools that provide immediate value. Focus on usability over complexity with clear inputs, instant results, and helpful context that guides decision-making. • Implement SoftwareApplication schema using JSON-LD markup to identify your tool as an interactive application. Include name, URL, and operating system properties for AI comprehension. • Provide contextual explanations below calculators that AI systems can reference and cite. Structure content to be easily quotable while supporting the tool functionality. • Build tool authority through citations from industry sources and forums. Interactive tools that solve real user problems get linked by AI systems seeking functional solutions. Calculator-driven lead generation is becoming a distinct competitive advantage because AI systems prefer functional solutions over static content for action-oriented queries. What calculation problems do your prospects face? Are you seeing any interactive tools get recommended in AI responses for your industry? 👇