User Experience for Educational Tools

🎢 Onboarding UX Playbook (+ Decision Trees). Practical techniques for better onboarding UX, design patterns, kits and Figma templates — on mobile and desktop. 🚫 Users often skip tutorials/walkthroughs entirely. 🚫 Never block the UI with full-page onboarding modals. 🚫 Avoid long multi-step tutorials with 5+ steps. ✅ Ask customers what goals they are trying to achieve. ✅ Allow users to hide walkthroughs and restore them later. ✅ Focus on bringing users to first success moments fast. ✅ Structure your onboarding suggestions in bite-sized chunks. ✅ Explain features when users slow down or make mistakes. ✅ Show features when users lose time with repetitive tasks. ✅ Prevent failure with an early warning system for new users. ✅ Collapsible checklists work well for onboarding. ✅ Personalized onboarding works even better. ✅ Design sets of filters, templates and empty states. ✅ Show starter kits based on user’s profile and interests. ✅ Consider short video guides and email drip campaigns. Good onboarding can’t be generic. It has to be relevant and valuable. Define your user segments first. Design a set of presets to help them get to success moments faster. Think of the questions you need to ask to customize their experience. Think about filters and presets they might need. Onboarding tutorials often appear once and get instantly dismissed, nowhere to be found again. Allow users to find them when they need it. Bring them up when users slow down or make mistakes. And test the discoverability of your features continuously. If a feature is obvious, you might not need to explain it at all. And if it isn’t, perhaps onboarding won’t solve this problem either. Useful resources: How to Choose Onboarding Methods and Components, by NewsKit 👍 Methods: https://lnkd.in/eWn5FPWA Decision Tree: https://lnkd.in/e8TmMDFf Design Patterns: https://lnkd.in/ed7HjzkW Onboarding UX Playbook, by Eleana Gkogka https://lnkd.in/edcDfMFG Complete Onboarding UX Guide (free eBook), by Intercom https://lnkd.in/eAxT6ZM4 User Onboarding Best Practices, by Taras Bakusevych https://lnkd.in/eRwr2tEc Guide to Onboarding, by Phil Byrne https://lnkd.in/esEavgw7 How Spotify Organizes Onboarding in Figma, by Barton Smith, Cliona O'Sullivan https://lnkd.in/ei434tqq Mobile Onboarding Wireframe Flows (Figma template) https://lnkd.in/ekhzWFJz UX Onboarding Patterns, by Eve Weinberg https://lnkd.in/e7_M4kDv #ux #design

Why the blueberry muffin accessibility analogy works so well for learning content. I still find the blueberry muffin analogy one of the best ways of explaining why it's so important to consider accessibility from the start of a learning project. Of course, you can add in accessibility afterwards, but imagine pushing those blueberries in by hand after the muffin is cooked. Not only does it feel like an afterthought for the learner, but it's also frustrating and time-consuming for the practitioner! In my recent conversation with Bill Banham on the Voices of the Learning Network Podcast, we explored what baking accessibility in from the start looks like - and how accessible design leads to better outcomes for all learners. We discussed simple ways to include accessibility in your everyday practice: - Writing clear, descriptive alt text that adds context. - Providing accurate captions and transcripts that benefit everyone. - Using consistent heading levels so learners and assistive technologies can navigate easily. - Applying good colour contrast. - Using plain language to reduce cognitive load. We also explored practical ways AI can help practitioners apply accessibility and why leadership matters for modelling inclusion, celebrating progress, and embedding accessibility into standards and strategy. When research shows that up to a quarter of your learners may have a disability or experience a temporary or situational access need, accessibility becomes more than a nice-to-have - it's a fundamental part of excellent learning design. So the next time you design a course, remember the blueberry muffin. Accessibility isn't an ingredient to add in at the end - it needs to be baked in from the start. You can listen to my full conversation with Bill at the link below: https://lnkd.in/eiBeiTEr #eLearning #Accessibility #AccessibleLearning #eln (Blueberry muffins on a wooden surface, with fresh blueberries scattered nearby. Baked blueberries are generously distributed through the batter of the muffins, creating deep purple pockets.)

Learning journeys are not built in a day. But they can be built with a system. I created the G.R.O.W.T.H. Framework to help learning designers map experiences that actually stick. Most models stay in theory. G.R.O.W.T.H. is a toolkit you can take into your next project and put to work. Here is what you will find inside: ✅ Six-stage framework to map your journey ✅ Goal-setting worksheet for stakeholder alignment ✅ Empathy mapping template ✅ Learner feedback form ✅ Team retro guide ✅ Real-world case study to show it in practice This is a free download. You will find the full PDF attached to this post. If you are building learning journeys for onboarding, upskilling, compliance, or customer education, this gives you a clear structure to follow. Simple. Practical. Designed to be used. Scroll through the document and tell me what you think. I would love your feedback.

🔎 Accessible Usability Scale (AUS): Prioritizing Inclusive Usability The Accessible Usability Scale (AUS) is a focused, accessibility-centered usability metric designed specifically for users of assistive technologies. Developed by Fable and launched in 2020, AUS builds on the foundation of SUS — but adapts it to capture how inclusive and accessible digital products really are from the perspective of people with disabilities. 1️⃣ Collecting Feedback from Assistive Technology Users Collect responses from users who rely on assistive tech like screen readers, screen magnifiers, voice control, or switch devices. The questionnaire includes 10 statements, each rated on a 5-point Likert scale from “Strongly Disagree” (1) to “Strongly Agree” (5). Statements focus on frustration, navigation, clarity, and task completion with assistive tools. 📚 For a deeper dive into the questionnaire, you can explore the official AUS resource page provided by Fable: 2️⃣ Calculation Scoring AUS is nearly identical to SUS: • For positive items (1, 3, 5, 7, 9): (response - 1) × 2.5  • For negative items (2, 4, 6, 8, 10): (5 - response) × 2.5   • Total score = Sum of all 10 items → Range: 0–100 🎯 Score Meaning:  Higher score = better perceived usability for assistive tech users. 3️⃣ Interpreting the Results Fable’s data across 2,100+ sessions suggests considering the average AUS score is 65 and the average across different assistive technologies: • Screen Magnifier Users → 72 • Alternative Navigation Users → 67 • Screen Reader Users → 56 🔎 Pros & Cons of Using AUS ✳️ Advantages: • Accessibility-focused – designed specifically for AT users. • Simple & Familiar – Based on SUS; quick and easy to implement as well as for participants to complete. • Produces quantifiable scores and can be used alongside qualitative feedback for depth. • Free and open for anyone to use. Licensed under Creative Commons. Attribution 4.0 International (CC BY 4.0) → Can be used, adapted, and shared, as long as you give appropriate credit to Fable. • Complements SUS in broader usability testing. ❌ Disadvantages: • Reflects feelings and perceived ease of use, not technical accessibility compliance. • Most useful post-task (after completing flows or sessions). Users must interact deeply with the product for results to be meaningful. • Not as widely adopted or recognized as SUS (but gaining traction). The illustration in the document is sourced from the official website. 💬 Have you tried using AUS or included AT users in your UX research? 👇 Share your thoughts below & check references in the comments. #Accessibility #AUS #InclusiveDesign #UX #UXMetrics #AssistiveTechnology #UsabilityTesting #ProductDesign

Working on instructional invariants today and the idea that evaluability is far more important than feedback. In fact, feedback is not an invariant at all. An instructional invariant is a non-negotiable design condition that must hold for learning to occur reliably. If violated, it causes learning to fail — even if everything else appears to be working. Instructional invariants are constraints on learning environments that prevent predictable failure. Feedback is something the system does. Evaluability is something the learner does. There’s a massive difference between the two. You can drown a learner in feedback and still leave them saying: “I’m trying, but I don’t know what I’m doing wrong.” Evaluability means the learner can tell whether their response is correct or incorrect in a way that allows them to actually do something about it. In other words, they can evaluate what is going on. It needs to be: detectable, localisable, interpretable and actionable. If learners cannot evaluate their performance, learning becomes unreliable — regardless of how much feedback is provided. Put simply: feedback ≠ evaluability. This matters because learning is not the default outcome of exposure. Learners will always choose the cheapest way to reduce error. If they can’t evaluate their performance, they switch strategies or disengage. From a design perspective, this is why “more feedback” often fails. It increases noise without sharpening the error signal. In other words, the problem isn’t quantity. It’s precision. Evaluability is a core instructional invariant. Learning can fail even when learners are active, motivated, and using the right skill. If they don’t know what’s wrong and how to fix it, feedback is useless. So the design question isn’t: “Did we give feedback?” It’s: “Did we make correctness and error visible, interpretable, and actionable?” I think this matters even more with edtech because, again, learning is not the default outcome of exposure. In apps and AI systems, learners will almost always take the cheapest path to reduce error — guessing, pattern-matching, prompt-surfing, etc. — if evaluability is weak. Ultimately, feedback is a method, not a condition. You can have: lots of feedback detailed feedback well-intentioned feedback even “high-quality” feedback …and still leave the learner unable to answer: Was I right? What exactly was wrong? What do I do differently next time? That means learning stalls, even though “feedback” occurred. So feedback fails the invariant test: Learning can fail even when feedback is present. Therefore, feedback cannot be an instructional invariant.

Designing inclusive learning scaffolds with digital tools isn’t an add-on — it’s essential. When aligned with Universal Design for Learning (UDL), digital scaffolds can remove barriers and give every learner meaningful access to content. When creating digital scaffolds, try to: ✨ Add text instructions that remind students they can use built-in iPad tools like Speak Selection or Translate ✨ Provide audio directions so pupils can replay instructions ✨ Include clickable word banks or further reading links ✨ Use consistent visual icons to signal support ✨ Offer placeholders for written or audio responses ✨ Add alt text so images can be read aloud Inclusive design isn’t about lowering challenge — it’s about increasing access. #UDL #InclusiveEducation #EdTech #Accessibility

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.

Selecting the right AI tool can be challenging when new products appear almost daily. This guide helps you cut through the noise with a clear, structured process for testing, evaluating, and integrating AI tools in the classroom. It introduces a practical framework built around three pillars: usability, pedagogy, and ethics. Each is broken into a checklist of focused questions to help educators quickly determine whether a tool fits their curriculum, supports deep learning, and meets privacy standards. The guide also includes tips for piloting tools with a small group, gathering student feedback, and reflecting on results. This guide is informed by key resources, including aiEDU’s AI Readiness Framework, ISTE’s Teacher Ready Edtech Product Evaluation Guide, the U.S. Department of Education’s AI Integration Toolkit, and UNESCO’s Recommendation on the Ethics of AI. These references shaped the usability, pedagogy, and ethics checklists to keep the framework practical and research-based. #AIinEducation #EdTech #TeachingWithAI #TeacherTools #AIforTeachers #EdLeaders #ClassroomInnovation #DigitalLearning #AIIntegration #EducationTechnology

Most product teams kill great ideas with the wrong evaluation process. They either: → Ship MVPs that never evolve → Over-engineer before validating → Miss the signal in noisy metrics After managing 50+ product launches across FinTech, EdTech, & Logistics, I've refined a framework that works across every stage: MVP → MMP → MAP. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 (𝗣𝗘𝗙) Six dimensions.  One decision framework. 1. 𝘝𝘢𝘭𝘶𝘦 𝘍𝘪𝘵 — 𝘋𝘰𝘦𝘴 𝘪𝘵 𝘮𝘢𝘵𝘵𝘦𝘳? Does this solve a real problem worth solving? → Problem-solution fit validated → Clear user pain addressed → Expected business outcome (ROI, revenue, cost savings) → Early adopter feedback collected Score: Low / Medium / High 2. 𝘜𝘴𝘢𝘣𝘪𝘭𝘪𝘵𝘺 𝘍𝘪𝘵 — 𝘊𝘢𝘯 𝘱𝘦𝘰𝘱𝘭𝘦 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘶𝘴𝘦 𝘪𝘵? Beautiful features mean nothing if users struggle. → UX simplicity tested → Accessibility compliance checked → Key flow completion rates measured → Support tickets analyzed Score: Poor / Acceptable / Excellent 3. 𝘍𝘦𝘢𝘴𝘪𝘣𝘪𝘭𝘪𝘵𝘺 𝘍𝘪𝘵 — 𝘊𝘢𝘯 𝘸𝘦 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 𝘪𝘵 𝘳𝘦𝘭𝘪𝘢𝘣𝘭𝘺? Technical stability determines long-term success. → Architecture scalability reviewed → Performance benchmarks met → Security & compliance validated → Tech debt quantified → Maintainability assessed Score: Red / Yellow / Green 4. 𝘔𝘢𝘳𝘬𝘦𝘵 𝘍𝘪𝘵 — 𝘞𝘪𝘭𝘭 𝘪𝘵 𝘨𝘳𝘰𝘸? Early traction isn't the same as sustainable growth. → Market size & demand validated → Competitive differentiation clear → Adoption trend positive → Expansion potential identified → Retention probability high Score: Low / Moderate / Strong 5. 𝘍𝘪𝘯𝘢𝘯𝘤𝘪𝘢𝘭 𝘍𝘪𝘵 — 𝘐𝘴 𝘪𝘵 𝘸𝘰𝘳𝘵𝘩 𝘵𝘩𝘦 𝘪𝘯𝘷𝘦𝘴𝘵𝘮𝘦𝘯𝘵? The brutal question every CFO asks. → CAC vs LTV healthy → Cost of build vs cost of delay calculated → Pricing model validated → Revenue forecast realistic → Ongoing OPEX sustainable Score: Not Viable / Viable / Highly Viable 6. 𝘖𝘱𝘦𝘳𝘢𝘵𝘪𝘰𝘯𝘢𝘭 𝘍𝘪𝘵 — 𝘊𝘢𝘯 𝘵𝘩𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘴𝘶𝘱𝘱𝘰𝘳𝘵 𝘪𝘵? Great products fail when operations aren't ready. → Sales enablement complete → Support team trained → Documentation ready → SLAs defined → Monitoring in place Score: Not Ready / Partially Ready / Fully Ready 𝗧𝗵𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗠𝗮𝘁𝗿𝗶𝘅: Calculate your Evaluation Score = (Value + Usability + Feasibility + Market + Financial + Operational) / 6 Then decide: ✅ Go → Move to MMP or MAP 🔄 Grow → Improve & iterate ⏸ Pause → Fix critical blockers 🛑 Stop → Pivot or sunset Why this works: - Most teams evaluate products emotionally or politically. - This framework forces objective, multi-dimensional assessment before you burn budget & morale on the wrong bet. Your turn: What’s the most overlooked dimension? 📌 Follow Santonu Mukherjee for 𝗚𝗲𝗻𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 digital 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 stories. 🔄 Repost & 👍 Like if you enjoyed reading. #ProductManagement #DigitalTransformation #ProductStrategy #Innovation #GenAI #Leadership

If your onboarding feels clunky, confusing, or last-minute… your client can feel it too. The work doesn’t begin after the payment. It begins the moment someone says “yes.” And this is where most people drop the ball. I’ve been there too. Until I started using AI to simplify, personalize, and hold space for my onboarding flow, without losing the human in the process. Here’s what that looks like: Step 1: Welcome, with intention: As soon as a client signs up, I feed their context to ChatGPT: “Write a warm welcome email to a new client who just signed up for [X service]. Acknowledge their goals, set the tone for our work together, and share what to expect this week.” It helps me start the relationship right, with presence, not a template. . . . Step 2: Kickoff kit, custom to them Instead of sending a generic Notion board or onboarding doc… I use AI to create a personalized one-pager: - Their name, goals, timeline - Pre-work checklist - Tools we’ll use - Access links - FAQs based on their niche It makes them feel seen. . . . Step 3: Pre-call prep that’s actually useful If I’ve collected form answers or voice notes, I prompt: “Summarize this client’s challenges and suggest 3 angles I should explore in our kickoff call.” I walk into the call aligned and calm. They feel it. . . . Step 4: Clarity recap - fast After the call, I feed my notes to ChatGPT: “Turn this into a call recap email with clear next steps and aligned expectations. Keep it real, not robotic.” It saves 30 minutes of staring at the screen and helps me build trust in the tiny details. . . . Step 5: Ongoing onboarding, quietly handled Need reminders? Nudges? Status updates? I’ll set up small AI workflows that keep things moving without nagging or micro-managing. Because onboarding isn’t a task. It’s the first chapter of your client experience. You don’t need AI to replace the way you work. But you can use it to hold the edges, so you show up more fully in the middle. That’s what onboarding should feel like. Intentional. Warm. Clear. And deeply human. If you want the actual AI stack I use to support this flow (without feeling cold or corporate), comment "ONBOARD" or DM me and I’ll send it over. Follow Vartika Mishra !