Online Course Design

AI is reshaping the future of learning, not by replacing educators, but by amplifying human potential. I just read Google’s new position paper on 'AI and the Future of Learning', and several points resonate strongly with my own experiences in e-learning, agentic AI, and responsible innovation. Key takeaways for educators, learning designers and AI practitioners:- 1. Human-in-the-loop matters:- AI should empower teachers and learners, not supplant them. Educators remain central in designing, customizing, and supervising AI tools. 2. Personalized, adaptive learning:- AI can meet learners where they are, adapt to their pace, strengths, and needs, especially powerful in large scale or resource-constrained settings. 3. Ethics, fairness, transparency:- Tools must be built responsibly, transparent about data usage, bias, and decisions. Learners, teachers, and their families should understand how AI arrives at suggestions and always have recourse. 4. Skills for the future:- Beyond knowledge recall, education needs to foster curiosity, metacognition, collaboration, and lifelong learning. AI becomes a partner in cultivating how we learn, not just what we learn. As someone who leads e-learning and agentic AI initiatives (and working on courses / frameworks for learning system design), here are some reflections:- 1. Design with pedagogy first:- When building courses or tools, we must anchor in learning science and best practices. Agents or AI modules should align with what we know about how people learn, including cognitive load, scaffolding, and feedback loops. 2. Build with practitioners:- Co-design with educators ensures the AI tools remain grounded in context, and helps avoid misalignment or unintended biases. 3. Measure impact holistically:- Beyond completion or test scores, we should evaluate growth in learner agency and self regulation, especially for adult learners or professionals. 4. Scale responsibly:- The potential for scaling personalized learning is huge, but we must not lose sight of the social, cultural, and equity aspects of learning design. 🧭 In my upcoming course on Augmenting Collective Intelligence via Autonomous Agents + Human Experts, I'll integrate several of these insights:- embedding AI tutors in training, designing feedback loops, and ensuring alignment with ethical & pedagogical frameworks. 💡 Question for my network:- How are you balancing AI tool adoption in education or training environments while preserving educator control, equity, and learner agency? Would love to hear your experience or frameworks that are working. #AI #EdTech #LearningDesign #AgenticAI #LifelongLearning #InstructionalDesign #AIgovernance

What if an online course didn’t just teach innovation but operated like a product studio? That’s the design ethos behind New Product Development, a fully asynchronous course I am developing within the Master of Business Management at the University of Auckland. It’s not just about learning innovation theory. It’s about practising innovation as a way of learning and doing so in a way that fits the realities of working professionals. The course unfolds through six studio sprints, each aligned with a real-world product development stage: 🔹Framing opportunities 🔹Discovering unmet needs 🔹Designing value 🔹Building prototypes 🔹Go to market strategy 🔹a final innovation portfolio and pitch Every sprint includes hands-on toolkits, reflection prompts, and optional peer critique. Assessments are artefacts: opportunity maps, personas, low-fidelity prototypes, validation plans, and strategic pitches. These artefacts mirror what students might produce in a product team, innovation unit, or consultancy. But what makes this possible online? I’ve reconceived Canvas LMS not as a content repository but as a virtual studio: 🔹Sprint dashboards replace linear modules. 🔹Toolkits and templates scaffold creative work. 🔹Discussions become “crit walls” for sharing work-in-progress. 🔹Reflection journals trace how students make decisions in uncertain contexts. The pedagogy draws from studio-based learning, design thinking, and agile methodologies but adapted for asynchronous learners. This means no Zoom fatigue, no live workshops, and no assuming everyone’s working on the same schedule. Instead, students build momentum through iterative, flexible engagement directly tied to their own industries, roles, and contexts. Why does this matter? Because the students in this course are not full-time students—they are full-time professionals. Product managers, consultants, public servants, engineers, and social innovators. For them, learning must integrate into the flow of work, not interrupt it. Studio pedagogy allows that. It invites them to explore workplace-relevant challenges, use generative AI ethically and creatively, and produce outputs that can feed back into their own projects. It’s one thing to talk about lifelong learning. It’s another to build courses that make it practical, applied, and meaningful. That’s the promise of studio-based, asynchronous design. I believe it’s a model with broad relevance, far beyond product development. #OnlineLearning #StudioPedagogy #LearningDesign #CanvasLMS #InnovationEducation #ProductManagement #HigherEducation #WorkIntegratedLearning #AsynchronousLearning #EdTech #AIinEducation #Universities

We’re still trying to catch AI “cheaters” instead of reimagining what learning could be. 🤔 And, many educators feel stuck in policing mode—focused on detecting AI in essays and assignments. Meanwhile, students? They’ve already moved on. They’re using AI to brainstorm, structure arguments, get feedback—and yes, even deepen their understanding. 💡 What if we stopped treating AI as a threat... and started using it as a thinking partner? That’s the shift Anthropic’s Claude for Education is embracing. It doesn’t hand out answers like other LLMs. It prompts students to think their way there—with thoughtful questions, reflective cues, and a pedagogy rooted in guidance over answers. That same philosophy shapes how I have been designing my Course Buddy: 🔹 Built to collaborate — working with the student, not for them 🔹 Built to guide — encouraging reflection and deeper inquiry 🔹 Built to teach thinking — focusing on the process, not just the outcome The future of assessment won’t be about catching AI use. It’ll be about designing for it—with pedagogy that evolves alongside the tools. 👉 Who is leading that shift? And who risks being left behind? #AIinEducation #EdTech #ClaudeAI #Pedagogy #CriticalThinking #Assessment #FutureOfLearning #Teaching #HigherEd #Anthropic #Innovation #LearningDesign

New research (Gerlich 2025) published last week confirms that structured training on prompting & thinking with AI makes it much more likely students will use it as a dialogic partner rather than a cognitive shortcut. Guided support followed these crucial 5 steps: 1. Participants initial unaided reflection (i.e. no AI access) on a preset task. 2. Use of ChatGTP for targeted research to develop context and fact-finding with constrained prompts. 3. Participants revision of original reflections to improve argument construction (without copy-paste access). 4. Critical review of new hybrid outputs using ChatGTP to stress test argumentation. 5. Final revision and reflection with ChatGTP rooted in participants' reasoning and judgement. This process reduced offloading, created higher rubric-rated critical reasoning and higher self-reported reflective engagement. Guided use also narrowed demographic performance gaps and produced what participants described as a “seminar-style challenge”. Another interesting finding . . . Users in other test groups suffered an illusion of non-offloading: they thought they were doing the cognitive work while their behaviours showed otherwise. Interaction design is clearly key. Structured support is a valuable adoption lever that preserves student agency and fosters deeper learning when AI is rolled out at scale. Full study here: https://lnkd.in/dfE7WKsf

The engagement gap: why traditional online learning metrics hide the real reason students disengage. Most platforms track completion rates. But they miss what really matters. Isolation kills motivation faster than any technical glitch. Here's how to build real connection in virtual spaces: 1️⃣ Community-First Design • Break the solo learning trap • Foster peer relationships • Create belonging through structure ↳ Group projects that actually work ↳ Guided discussions that spark dialogue ↳ Micro-communities that stick together 2️⃣ Real-Time Connection Points • Schedule virtual coffee chats • Host informal study groups • Break down social barriers ↳ Weekly check-ins build momentum ↳ Informal spaces encourage bonding ↳ Small groups maximize interaction 3️⃣ Peer Support Networks • Match learners strategically • Enable organic mentoring • Build accountability partnerships ↳ Buddy systems drive completion ↳ Peer feedback loops work magic ↳ Support circles prevent dropout 4️⃣ Active Instructor Presence • Show up consistently • Engage authentically • Guide conversations naturally ↳ Regular office hours matter ↳ Personal responses build trust ↳ Active participation sets the tone 5️⃣ Inclusive Space Design • Clear community guidelines • Diverse representation • Accessible support systems ↳ Everyone feels welcome ↳ All voices get heard ↳ Support reaches everyone The secret isn't more content. It's better connection. Build community first. Everything else follows. How are you designing for connection—not just completion—in your online learning spaces?

If Teachers Don’t Get AI, Our Kids Won’t Either. Full Stop. The “Demystifying AI” study examined five short, free online AI professional-development (PD) courses for K–12 teachers in #Colombia, #Cyprus, #Ghana, #Greece, #Uganda, the #UnitedStates, and #Qatar, created by World Innovation Summit for Education (WISE) with the MIT PKG Center for Social Impact–12 Initiative and MIT RAISE 🎓. PD here means structured learning experiences that help teachers strengthen skills and bring new practices into the classroom 📚. Using randomized course assignment and pre/post surveys, the researchers explored how course design, language, timing, and delivery influence teachers’ AI knowledge, confidence, and ethical awareness, and how scalable, low-cost PD can support responsible, equitable use of generative AI with students 🌍🤖. 1. 🚀 AI PD boosts practical classroom readiness Short, flexible online AI professional-development courses increased teachers’ comfort using generative tools, crafting prompts, and designing classroom activities for students. 2. 🧠 Conceptual gaps persist in core AI ideas Teachers still struggled with core AI ideas like training data, models, and bias, retaining misconceptions even after completing courses online. 3. 🌎 Language, design, and credentials drive engagement Official translations, simple navigation, mobile-friendly design, and recognizable certificates encouraged higher enrollment, sustained engagement, and positive word-of-mouth among participating teachers. 4. 👩💻 Teacher profiles and infrastructure shape support needs Different teacher experience levels and local infrastructure shaped needs; many required basic digital skills support before engaging with AI content. 5. 🤝 Teachers want sustained, social learning ecosystems Participants valued flexibility, bite-sized modules, downloadable resources, and peer interaction, requesting ongoing communities of practice and follow-up opportunities for learning. Policy recommendations: 📘 Build AI PD frameworks co-designed with teachers and researchers. 🎯 Offer tiered PD pathways matching teachers’ readiness, and experience. 🌐 Guarantee multilingual courses with translations and relevant classroom examples. 🏅 Recognize AI PD certifications linked to progression and incentives. 💻 Invest in connectivity, devices, low-bandwidth platforms, offline-accessible materials everywhere. 🔐 Embed modules on data privacy, bias, and responsible AI. 👩🏫 Support hybrid PD combining asynchronous content with live mentoring. 🤝 Fund teacher communities of practice and peer-led learning networks. 📚 Align AI PD content with curricula, standards, and reforms. 📊 Monitor PD impact with surveys, classroom evidence, continuous improvement. Source: https://lnkd.in/enZN-CuM

Asynchronous Active Learning Strategies Active learning can thrive in fully online asynchronous environments with the right structure and scaffolding. Here are several strategies that work particularly well when students are not meeting in real time: 💎Structured, Multi-Step Discussion Prompts Design prompts that require students to do something before they post, e.g., analyze a case, annotate a reading, or complete a short activity. Then require a follow-up synthesis reply so they build on peers’ ideas rather than simply posting once. 💎Collaborative Annotation Use tools like Hypothes.is to let students co-annotate articles, videos, or documents. This creates a dynamic “conversation layer” over the text and supports deeper engagement than traditional forums. 💎Asynchronous “Think-Pair-Share” Students submit an initial individual response (“think”), are assigned a partner to exchange reactions with (“pair”), and then collectively post a synthesized contribution (“share”) to the class forum. 💎Role-Based Asynchronous Debates Assign students roles (stakeholder, critic, advocate, policymaker) and have them submit short position statements, counterarguments, and final reflections. Works well with audio/video posts, not just text. 💎Student-Generated Micro-Content Students create short explainer videos, infographics, or concept summaries and post them to a shared class gallery. Peers comment or “peer-tag” connections between different concepts. 💎Scenario-Based Branching Activities Use Padlet to introduce case studies or branching decision tasks. Ask students to choose their next step individually, then post a justification of their choices and compare pathways with classmates. 💎Online Jigsaw Adaptation Groups are assigned different resources asynchronously. Each student produces a short brief or artifact; then groups curate a combined “class resource hub” so all students access and learn from each part. 💎Peer Review with Rubrics Students upload drafts or artifacts and use a structured rubric to review peers’ work. This reinforces understanding of criteria and helps them internalize the learning outcomes. 💎Asynchronous Mini-Challenges After short, recorded lectures, give a quick “apply it now” challenge, e.g., solve a problem, critique an example, or choose the best option and justify why. Students post their solution and respond to two peers. 💎Learning Journals or Video Reflections Weekly low-stakes journals or 2–3-minute videos where students connect course concepts to their experiences, readings, or professional contexts. 👇Continued in the comments. Please scroll down to read more.👇 #ActiveLearning #OnlineLearning #AsynchronousLearning #DigitalPedagogy #InstructionalDesign #LearningDesign #EdTech #HigherEd #CollaborativeLearning #StudentEngagement #FacultyDevelopment #LearningStrategies

For decades, online learning has been held back by two major issues: Relevance & Presence. 1. The issue with relevance. Most online courses are "one size fits all," which ignores the personal motivations that drive true learning. When content feels relevant (tailored to the individual) it boosts engagement and makes learning truly meaningful. 2. The issue with presence. Research shows that feeling connected to a teacher or coach is key to motivation. Yet, most online learning lacks this element, and leaves learners without the responsive support they need. While tools like Coursera's Guided Projects have tried to address this, they haven’t fully captured the “always-on” connection a true coach provides. So, here’s how AI agents are bringing relevance and presence back into e-learning: 1️⃣ AI agents can adapt to each learner’s needs, so it can offer custom content and recommendations. Instead of forcing everyone through the same modules, agents guide learners through material that fits their skill level and learning pace. 2️⃣ Learners have 24/7 access to help. Stuck on a concept? Need clarification? An agent can offer explanations, suggest resources, and even provide follow-up questions to ensure understanding. 3️⃣ Soon, learners will be able to ask questions, get answers, and navigate content entirely through voice. Imagine saying, "Tell me more about this concept" or "What’s the next step?" and having an AI coach respond instantly with thought-out, relevant information. 3️⃣ Advanced agents can sense when a learner might be struggling or losing interest, and they’re equipped to offer encouragement, ask clarifying questions, or suggest a short review. 4️⃣ With the ability for multiple agents to work together, learners can receive support from “specialists” on different topics, creating a comprehensive, well-rounded experience. For instance, one agent may handle technical questions, while another focuses on motivation and goal-setting. The result? Smarter, more personalised, and more effective learning experiences. No, they won’t fully replace human coaches. However, they’ll fill a massive gap by providing much of the support, guidance, and feedback that keeps learners engaged.

Too often, we design Higher Education for the “traditional” 18–22-year-old. But the reality is that adult learners—25 and older—are nearly 1 in 4 undergraduates in the U.S. (3.9 million students in Fall 2023). They’re not the exception. They are the present and future of higher education. The Realities Adult Learners Face -69% of adult undergrads work while enrolled; nearly half (48%) have children, compared to just 3% of younger students (BestColleges). -42% of online learners are 30+, with the average age of online students in the U.S. at 32–34 (Devlin Peck, Colorlib). -25% of all students now study exclusively online, and another 27% take some online courses (Jobs for the Future). Flexibility is no longer optional—it’s the baseline. The Payoff Is Real -Adults who return and finish a bachelor’s degree increase their employment likelihood by 9.8 percentage points, work 2.2 more weeks per quarter, and earn $5,392 more annually (NSC). -Bachelor’s holders earn $1,493 per week on average—$500 more than peers with some college but no degree (BLS). -Adults who return see a 140% greater salary increase than those who don’t (George Fox). -With the right supports, 75% of returning adults complete a credential (NSC). What Works -Flexible pathways: online, hybrid, evening, and competency-based options. -Recognition of prior learning to reduce cost and time. -Employer partnerships offering tuition reimbursement and career-linked credentials. -Wraparound supports: advising attuned to working adults, childcare, and financial aid that fits family realities. -Stackable credentials that provide career gains while building toward degrees. The Bottom Line Adult learners are driven, resilient, and clear on their goals. The data shows they succeed—academically and economically—when institutions design around their needs. Supporting them is not just good practice; it’s essential for enrollment, equity, and workforce readiness.