Online Learning Platforms

So...can Claude Design replace Storyline’s view mode and try mode for software simulations? That was the question I wanted to answer this week. If you’ve ever built software training in Articulate Storyline, you know software simulations can be incredibly effective learning tools. You also know that Storyline’s view mode and try mode can be frustrating to create and maintain, especially once you move beyond simple click interactions. Text entry fields can be particularly painful. Small interface changes often require re-recording screens, rebuilding hotspots, adjusting feedback layers, or recreating parts of the simulation altogether. And when software interfaces are changing right up until launch, maintaining those simulations can become a project all by itself. What made this experiment particularly interesting is that it actually started with Anthropic’s new Fable 5 model. Before it was pulled offline, I had a chance to test it by providing a series of screenshots from a fictional CRM platform along with a simple description of the workflow I wanted learners to complete. With a single prompt, it generated both a guided software demonstration and an interactive simulation where learners could practice the workflow themselves. It included animated cursor movements, on-screen callouts, feedback messages, hints, text-entry validation, and even offered to package the experience as a SCORM course. That immediately raised another question: If Fable 5 could do this, could I recreate something similar using Claude Design? So I opened Claude Design, uploaded the same screenshots, described the workflow, and started experimenting. After a few rounds of refinement, I had a working software simulation that included a guided demo mode, an interactive practice mode, feedback for incorrect clicks, hints after multiple attempts, synced audio narration, and a standalone HTML file that could be hosted on the web. What surprised me most wasn’t necessarily that it worked...it was how quickly I was able to iterate. Instead of recording screens and building interactions slide by slide, the workflow felt much closer to editing and refining. If a screenshot changed, I could update the project rather than rebuilding significant portions of it. So, do I think this replaces Storyline today? Not entirely. There are still important questions around accessibility, SCORM, LMS tracking, governance, collaboration workflows, and long-term maintenance. But each time I run one of these experiments, I find myself less focused on whether AI perfectly replicates existing authoring tools and more focused on how these workflows might fundamentally change the way we create learning experiences in the future. 🔗 Watch the full experiment here: https://lnkd.in/g9SpjHaj #InstructionalDesign #eLearning #LearningAndDevelopment #ClaudeDesign #AI #ArtificialIntelligence #eLearningDevelopment #ArticulateStoryline #VibeCoding #Fable5

T-Mobile made Voice sexy again. A few days ago, T-Mobile launched real-time voice translation. During a live call, you dial *87, and the conversation instantly shifts to one of 50 languages. No app, no special device, no download, everything runs inside the network. That is magic, but more importantly, that is architecture. For years, voice became a background utility. Unlimited minutes. Zero differentiation. OTT players innovated at the app layer while telcos carried traffic. Now inference moves into the media path. When AI runs natively in the network, the call becomes programmable. Translation is only just the tip of the iceberg. The same AI insertion point enables deepfake detection, voice biometrics tied to SIM identity, compliance monitoring, AI receptionists for SMEs, automated call summaries, spatial audio collaboration, speak-to-pay, or real-time intent routing. I listed 12 concrete cases. None of them is science fiction, and all of them are tied to clear revenue pools. Inferencing is coming to telco networks. Read more here: https://lnkd.in/eMd7hz8n

For those in interested in the area of food security and nutrition on the continent of Africa; I contributed to a massive open online course (MOOC) focused on the main systemic issues surrounding the search for food security in Africa. The MOOC was developed with UM6P - University Mohammed VI Polytechnic, AUF and Chaire Unesco Alimentations du monde, it addresses the main issues facing the continent today in relation to the issue including: 1. What impact does climate change have on food production? 2. What are the ways to reconcile sustainable agriculture and nutritional security? 3. How do the governance and structuring of the sector impact the objective of food security? 4.  What can be the contribution as a start-up in the sector?  The mini MOOC is accessible to everyone free of charge on the following platform: https://lnkd.in/ezmrCNpP   🎤 Speakers: Tantely Razafimbelo Nawfel ROUDIES ROBIN DUPONNOIS @Moussa SALL Evelyn Chiyevo Garwe Eli Sawadogo Julie Stoll Nicolas Bricas Nadia Ouaadi Joann Whalen @jessica allogo Myriam AIT-AISSA Lydia Merrouche Kaleab Baye Matthieu Brun @Sandrine Dury Victor Ongoma Olanike Adeyemo

Cartesia Sonic-3 is the first AI voice model I’ve seen that nails Hindi perfectly. For years, even the best text-to-speech (TTS) models struggled with Hindi. The rhythm, tonality, and emotional micro-expressions just didn’t sound human and the accent was inaccurate. This model doesn’t just translate Hindi. It is specially trained for it, with precise control over pacing, expressions and  tonality, all rendered in real time. Under the hood, Sonic-3 is engineered for low-latency voice generation optimized for conversational AI agents, clocking in 3–5x faster than OpenAI’s TTS while maintaining superior transcript fidelity. What makes it stand out technically: → 𝗚𝗿𝗮𝗻𝘂𝗹𝗮𝗿 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝘁𝗮𝗴𝘀 let developers dynamically modulate speed, volume, and emotion inside the transcript itself. ("Can you repeat that slower?" now works in production.) → 𝟰𝟮-𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗺𝗼𝗱𝗲𝗹 built on a single unified speaker embedding, so one voice can switch between languages like Hindi, Tamil, and English natively while maintaining accent continuity. → 𝟯-𝘀𝗲𝗰𝗼𝗻𝗱 𝘃𝗼𝗶𝗰𝗲 𝗰𝗹𝗼𝗻𝗶𝗻𝗴 powered by a low-sample adaptive cloning pipeline that enables instant personalization at scale. → 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘀𝘁𝗮𝗰𝗸 achieving sub-300 ms end-to-end latency at p90, tuned for live interactions like support agents, NPCs, and healthcare assistants. → 𝗙𝗶𝗻𝗲-𝗴𝗿𝗮𝗶𝗻𝗲𝗱 𝘁𝗿𝗮𝗻𝘀𝗰𝗿𝗶𝗽𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 that handles heteronyms, acronyms, and structured text (emails, IDs, phone numbers) which usually break realism in production systems. 🎧 Here is example of me trying Sonic-3’s Hindi. You have to hear it to believe it. If you’re building voice agents, conversational AI, or multimodal assistants, keep an eye on Cartesia. They’ve raised $100M to build the most human-sounding voice models in the world, and Sonic-3 just set a new benchmark for multilingual voice AI. #CartesiaPartner

I interviewed a dentist who believes that if we trained pilots the way we train dentists, they would all be dead. Lincoln Harris shared this provocative insight on the show today. His reasoning is simple: You cannot land a plane safely after just reading a book or watching one demonstration. You need thousands of repetitions in a simulator. But in dentistry, and often in medicine, we graduate professionals who have practiced limited repetitions on plastic teeth in artificial environments, then send them out to learn on real patients. Even continuing education often happens in hotel conference rooms, far removed from the reality of a clinic. Dr. Harris is changing this with cloud-based simulation training. It is essentially a flight simulator for dental surgery. Instead of flying to a teaching institute, dentists receive specialized mannequins to use in their own operatories. They practice complex procedures using their own chairs, their own lights, and their own instruments. They then upload high-resolution photos of their work to the cloud, where experts from anywhere in the world provide detailed feedback. The results are undeniable. Dr. Harris told me that while traditional courses have an implementation rate of about 15 to 18 percent, his method sees dentists implementing new procedures at four times that rate. It costs a quarter of the price and removes the need for travel. This isn't just about better fillings. It is about a fundamental shift in how we train surgeons. As Lincoln put it, everyone wants their pilot to have thousands of hours of experience, not just to have read thousands of papers. We should demand the same from our healthcare providers. 🎙️ Listen to "Why modern dentists must train like pilots" on The Podcast by KevinMD. (Link in the comments ⬇️) #KevinMD #Dentistry #MedicalEducation #PatientSafety #HealthTech

Health and safety people, please don't complain about not having budget for professional development. You might not get exactly what you want....but if you are prepared to take responsibility for your growth and development, I have a solution for you. I was chatting recently with someone who got some great value from a coaching workshop series I ran a few years ago on marketing health and safety properly. Their company paid for it, but that's not the point. It helped them think more broadly about their health and safety practice. When we overlap something like marketing into health and safety, some interesting and very useful things start to become clear, and help us improve our practice. That clarity led this person to tell me that they'd followed their nose, and they were getting interested in behavioural science. (That's the stuff made famous by 'Nudge Theory'). And I asked if they'd ever heard of a MOOC. They said no. I asked, because I did a MOOC years and years ago on behavioural science myself. It was really valuable. And it was free. A MOOC is a Massive Online Open Course. You can do an entire MBA program like that. You get the learning. (You just don't get the piece of paper at the end). And it's free. There are HEAPS of MOOC's out there, right at your fingertips. So, will you go and get what you want, instead of relinquishing your learning to someone else's budget? I know that a MOOC isn't the same as in-person. It's not the same as getting a certificate. It's not the same as having your employers 'support'. It's not going to be perfect. And you still need to do your research and pick a reputable institution. But if you want to take responsibility for your development and growth, there is literally nothing stopping you. So maybe your first step could be to Google 'Free MOOC + [topic of interest to you right now]' And see what's possible. #healthandsafety #professionalpractice #takeresponsibility

My DPhil research is shaping up... Fine-tuning large language models (LLMs) has revolutionized how we use AI, but let’s face it—it’s not perfect. Current methods demand too much: labeled data, computational resources, and time. Plus, they’re stuck in static environments. The result? Models that are powerful but rigid, unable to adapt to real-world, dynamic tasks. What if we could change that? My dissertation research proposes a groundbreaking method that integrates LLMs into simulation environments, combining self-training and reinforcement learning. Instead of relying on static datasets, these models learn dynamically, adapting to evolving scenarios. This approach reduces compute costs while improving metrics like perplexity and task success rates. It’s not just fine-tuning; it’s adaptive learning for AI that thinks on its feet.

Not all soft skills training is created equal. A few months ago, I was working with a group of managers from a large manufacturing company. They had been through plenty of training programs before- the kind where you take notes and then go right back to doing things the old way. When I walked into the room, I could see it in their faces: Let’s see if this is any different. So instead of starting with slides or theory, I took them straight into a live simulation: - A crisis scenario that could actually happen in their business. - Conflicting priorities, tough personalities, and limited time to decide. - Every move they made in real time had visible consequences. To begin with, I saw a lot of resistance in experimentation, voices which were not too loud and over powering were ignored leading to loss of critical information- the room was tense. People hesitated. Some stuck to their usual patterns. But as it got deeper, they started communicating much more effectively, this led to them collaborating, noticing blind spots, and eventually testing new ways to lead. By the end, they weren’t asking- Will this work? They said that they wanted to cascade it to their teams. Weeks later, I got an email from one of the managers. He told me he used the exact process from our simulation to navigate a real customer crisis and not only avoided a major fallout, but actually strengthened the client relationship through this crisis. That’s the difference between training that’s forgotten by the time you’re back at your desk, and training that rewires how you think, act, and lead. The secret? Immersion. When participants practice real scenarios, solve actual challenges, and see the impact of their decisions in the room, learning sticks. Priya Arora #immersivelearning #trainingdesign #employeeengagement #learningthatsticks #corporatelearning #leadershipdevelopment #upskilling #skillbuilding #workplacetraining #experientiallearning #Learningdeisgn #corporatetrainer #softskillstrainer #simulation #experintialtraining

The next voice interface will not just answer. It will do work while the conversation is still unfolding. Most voice products today still behave like a nicer IVR: listen, respond, wait. That breaks the moment a user changes context, asks for a multi-step task, or needs help across languages. OpenAI’s new Realtime Voice Models point to a different product pattern: voice agents that reason, call tools, translate, and transcribe live. Three things are worth watching: - Voice-to-action: say the goal, and the agent reasons through it, checks systems, and completes the task. - Systems-to-voice: apps turn live context into spoken guidance, not another notification. - Voice-to-voice: conversations keep moving across languages while people speak naturally. - Streaming transcription: captions, notes, and downstream workflows update before the meeting or call is over. The shift is not just better speech. It is latency, reasoning, tool use, and context collapsing into one interface. That changes what teams can build in support, travel, education, healthcare, sales, operations, and any workflow where typing is the bottleneck. Where do you think realtime voice agents will break first in production? #AI #VoiceAI #RealtimeAI #OpenAI #Agents

Introducing Adaptive Classifier: A new approach to text classification that learns continuously without catastrophic forgetting. Traditional ML systems require complete retraining when new categories emerge, leading to downtime and high costs. Our adaptive system changes this by adding new classes in seconds, not days. Key innovations: 🔹 Strategic Classification: First application of game theory to text classification, achieving 22.2% improvement in robustness against adversarial manipulation 🔹 Continuous Learning: Dynamic class addition without retraining, using prototype-based memory and neural adaptation layers 🔹 Production Ready: Built for real deployments with deterministic behavior, comprehensive monitoring, and seamless HuggingFace integration Real-world results: • Hallucination Detection: 80.7% recall for RAG safety applications • LLM Router: 26.6% cost optimization improvement through intelligent model selection • Content Moderation: Robust performance against gaming attempts The system combines prototype-based memory for fast adaptation with neural layers for complex decision boundaries. Elastic Weight Consolidation prevents catastrophic forgetting, while strategic cost functions model adversarial behavior. This addresses a critical gap in production ML systems where requirements evolve constantly. Instead of expensive retraining cycles, teams can adapt their classifiers instantly as new use cases emerge. Available as open source with complete documentation, examples, and pre-trained models. Links in the first comment below. #MachineLearning #ArtificialIntelligence #OpenSource #MLOps #TextClassification #HuggingFace #ProductionML #ContinualLearning