Curriculum Development Tools

Teaching children day-to-day house responsibilities in school can have a profound impact on their development and future independence. Here's why: *Benefits:* 1. *Life skills*: Children learn essential life skills, such as cooking, cleaning, and organization, which will benefit them throughout their lives. 2. *Responsibility and accountability*: Assigning household tasks helps children develop a sense of responsibility and accountability, essential for their personal and professional growth. 3. *Teamwork and collaboration*: Household chores encourage teamwork and collaboration, teaching children the value of working together to achieve a common goal. 4. *Time management and prioritization*: Children learn to prioritize tasks, manage their time effectively, and balance multiple responsibilities. 5. *Empathy and understanding*: By participating in household tasks, children develop empathy and understanding for the efforts of others, such as parents or caregivers. 6. *Preparation for independence*: Teaching children household responsibilities prepares them for independent living, reducing the likelihood of them relying on others for everyday tasks. 7. *Boosts self-esteem and confidence*: Completing household tasks can give children a sense of accomplishment, boosting their self-esteem and confidence. *Practical ways to incorporate household responsibilities into school curriculum:* 1. *Integrate life skills into existing subjects*: Incorporate household tasks into subjects like home economics, science, or math. 2. *Create a "Life Skills" course*: Offer a separate course focused on teaching household responsibilities, time management, and other essential life skills. 3. *Invite guest speakers*: Invite parents, caregivers, or professionals to share their expertise on various household tasks and responsibilities. 4. *Hands-on activities*: Organize hands-on activities, such as cooking, cleaning, or gardening, to teach children practical household skills. 5. *Encourage student participation*: Encourage students to take ownership of specific household tasks or projects, promoting responsibility and accountability. *Age-specific household responsibilities:* 1. *Preschool (3-5 years)*: Simple tasks like putting away toys, helping with laundry, or setting the table. 2. *Elementary school (6-10 years)*: Tasks like making their bed, helping with pet care, or assisting with simple meal preparation. 3. *Middle school (11-14 years)*: More complex tasks like loading the dishwasher, folding laundry, or preparing simple meals. 4. *High school (14-18 years)*: Advanced tasks like cooking meals, managing household budgets, or taking care of yard work. By incorporating household responsibilities into school curriculum, we can empower children with essential life skills, promote responsibility and accountability, and prepare them for independent living. #emotionalintelligence #teaching #school #counselling #parenting

Knowledge graphs to teach LLMs how to reason like doctors! Many medical LLMs can give you the right answer, but not the right reasoning which is a problem for clinical trust. 𝗠𝗲𝗱𝗥𝗲𝗮𝘀𝗼𝗻 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗳𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆-𝗴𝘂𝗶𝗱𝗲𝗱 𝗱𝗮𝘁𝗮𝘀𝗲𝘁 𝘁𝗼 𝘁𝗲𝗮𝗰𝗵 𝗟𝗟𝗠𝘀 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗖𝗵𝗮𝗶𝗻-𝗼𝗳-𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗖𝗼𝗧) 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝘂𝘀𝗶𝗻𝗴 𝗺𝗲𝗱��𝗰𝗮𝗹 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗴𝗿𝗮𝗽𝗵𝘀.  1. Created 32,682 clinically validated QA explanations by linking symptoms, findings, and diagnoses through PrimeKG.  2. Generated CoT reasoning paths using GPT-4o, but retained only those that produced correct answers during post-hoc verification.  3. Validated with physicians across 7 specialties, with expert preference for MedReason’s reasoning in 80–100% of cases.  4. Enabled interpretable, step-by-step answers like linking difficulty walking to medulloblastoma via ataxia, preserving clinical fidelity throughout. Couple thoughts:   • introducing dynamic KG updates (e.g., weekly ingests of new clinical trial data) could keep reasoning current with evolving medical knowledge.  • Could also integrating visual KGs derived from DICOM metadata help coherent reasoning across text and imaging inputs? We don't use DICOM metadata enough tbh  • Adding testing with adversarial probing (like edge‑case clinical scenarios) and continuous alignment checks against updated evidence‑based guidelines might benefit the model performance Here's the awesome work: https://lnkd.in/g42-PKMG Congrats to Juncheng Wu, Wenlong Deng, Xiaoxiao Li, Yuyin Zhou and co! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

The core ideas of GIS haven’t changed: data, analysis, visualization, sharing. But how we build and scale them has evolved dramatically. Traditionally, we used traditional desktop GIS for analysis and cartography. Now, many are shifting to Jupyter, GeoPandas, and QGIS. Tools that are open-source, scriptable, and interoperable. They make automation and reproducibility the default. Where ArcGIS Server once hosted services, the modern equivalent is filled by cloud storage (S3, GCS, Azure) combined with STAC catalogs, Iceberg, and other data lakehouse models. Open ecosystems where data and metadata can live side by side and be accessed by any tool. Instead of feature layers, we now use GeoParquet or Iceberg tables. These open formats separate storage from compute, allowing the same dataset to be analyzed in many upstream systems. The old WMS model of rendering images of maps on a server is being replaced by PMTiles, which let maps stream efficiently from object storage and make large datasets browsable instantly with no server backend. Where we once used "Web GIS" for hosting and sharing, today that role is And ArcPy, the scripting workhorse of desktop GIS, has evolved into a broader ecosystem of Python, SQL, Apache Sedona, PySAL, and DuckDB: powerful, lightweight, and built for integration with modern data workflows. What used to be proprietary and monolithic is now open, modular, and cloud-native. Data isn’t published, it’s queried. Maps aren’t hosted, they’re streamed. And analysis isn’t locked into one tool It’s scalable, scriptable, and shareable. The future of GIS isn’t a platform, it’s a stack. 🌎 I'm Matt and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 10k+ others learning from my newsletter → forrest.nyc

A program director once asked me: “How do we increase workforce student pass rates without increasing butt-in-seat time?” I said, “Give me a camera and a computer.” Here’s what I did: ↠ Recorded our instructors demonstrating the exact clinical skills students needed to learn. ↠ Gave students on-demand access so they could review whenever it worked for them. ↠ Created consistency between what they saw on screen and what they experienced in class. The result: ➜ Pass rates went up significantly. ➜ Students showed up more confident and prepared. ➜ We did it without adding more hours or burning anyone out. That was way back in 2017! This is why I believe in asynchronous, AI enabled, immersive learning. VR is distributed, repeatable, and experiential by nature. Better student outcomes come from better experiences, not longer lectures. #ImmersiveLearning #HealthcareEducation #EdTech #SimulationTraining #AsynchronousLearning VRpatients #workforceeducation

Our North Star at SmarterX is "Accelerating AI Literacy for All." Nowhere is that more important than helping parents and educators understand how AI impacts kids, whether that’s in the classroom or in their personal lives. Parenting in the age of AI, online gaming, communications apps, and social networking is hard. Even tech-savvy parents like myself struggle to keep up with privacy and safety best practices. Awareness of the risks and an ability to take actions that keep kids safe are critical. After reading a NY Times article this week about a 14-year-old boy who became obsessed with a Character AI chatbot and took his own life, I wanted to do more than talk about it. I wanted to help parents and educators take action. So, I built Kid Safe GPT for Parents, an online safety advisor to help parents understand and manage the risks, and protect their kids online. The GPT’s goal is to educate parents on the risks associated with digital interactions—from gaming to social media—and provide proactive guidance to help parents protect their children's mental health and safety. Kid Safe GPT offers practical advice, empathetic support, and tailored strategies to encourage healthy digital habits for families. There are three primary functions: 1) Understand Risks Learn about risks kids may face online. You can explore specific AI tools, apps, games, and social networks, or start with a general overview of common risks kids encounter in digital spaces, such as: cyberbullying, interacting with strangers, exposure to mature content, over-reliance on AI tools for schoolwork, and emotional attachment to virtual characters. 2) Talk to My Kids Get guidance on how to talk to your kids about online safety topics such as a general topic like understanding who they interact with online or how they’re using AI, or more specific concerns like privacy, gaming habits, or emotional well-being. 3) Create Guidelines Draft a general online safety agreement with your kids for all activities, or write a specific agreement for an app, game, AI tool, or social network. Kid Safe GPT is built on OpenAI’s ChatGPT, which can present information that sounds correct, but may be factually inaccurate or blatantly false (these are called “hallucinations”). You must apply your own judgment and critical thinking when using this tool to inform your decisions and actions. Kid Safe GPT is in beta and is only meant to be a starting point to accelerate awareness and safety. It is not meant to be a substitute for professional help on any issues related to your children’s online safety or mental health. I hope Kid Safe GPT helps you understand and manage the risks, while continuing to raise responsible and resilient children. See the comments below to learn more and access the tool. You will need a ChatGPT account to use it.

AI Literacy Toolkit for Families Artificial intelligence (AI) is shaping our children's world, from the videos they watch to the tools they use in school. Common Sense Media and Day of AI have teamed up to launch a free toolkit, featuring a video and interactive activities to help families and educators explore AI together. These resources support age-appropriate conversations and build the skills that kids need to thrive in a digital world. https://lnkd.in/gGFtgj4U

How I'm Teaching My Kids About Research Here’s an experiment I’m excited to share. 🚀 As part of homeschooling my two boys, we discuss current affairs every day—reading newspaper editorials and diving into world topics. Critical thinking and curiosity are at the heart of our learning. This week, we're taking it a step further with an engaging mini project: “The Role of Software in Taking Man to the Moon.” The rules? They’ll have full access to tools like Google, ChatGPT, and other resources. Their challenge is to research, take effective notes, and write a one-hour essay this Saturday. To kick things off, they turned to a seasoned expert for guidance: their aunt (Josephine E. Justin (Joyce)) , who happens to be a master in writing research papers and applying for patents. She walked them through tips on:  ✅ How to approach the research process  ✅ The art of organizing and taking useful notes  ✅ Structuring a final draft After their call, they documented all her golden advice in their personal “Notes Palace” (their designated digital space for organizing learning). Here are their notes: - https://lnkd.in/gbCizEWm - https://lnkd.in/gZeutexd Their mission Saturday is simple: pull together everything they’ve learned and deliver their best work. Who knows—they might even share their essays publicly on their website! I’ll keep you posted on how it goes. 💡 I believe this project isn’t just about learning history or technology—it’s about teaching them the lifelong skill of **how to learn** and develop independent thinking. How do you share learning experiences with kids? Would love to hear your ideas! 👇

In this post, I walk through how I used Claude AI to build two fully functional eLearning interactions — a tabbed specification comparison and a drag-and-drop knowledge check. I also cover how to host them free on GitHub Pages, embed them in Articulate Storyline or Rise, and share some honest thoughts on what this means for eLearning authoring tools and the LMS market.

Documentation is not paperwork. It is a tool for learning, reflection, and professional growth. Working in the development sector has taught me one thing very strongly: if we don’t document our work, we may remember the experience—but we may lose the learning. Over time, I have made documentation an integral part of my journey. Whether it is a field visit, a stakeholder meeting, a challenge faced, a successful intervention, a training session, or simply a reflection from the day, I try to document it. I regularly document: 🔹 My Learnings – What did I learn today that I didn’t know yesterday? 🔹 My Challenges – What didn’t work? What barriers did I face? What could I have done differently? 🔹 My Field Visits – What did I observe? What did stakeholders say? What patterns did I notice? 🔹 My Reflections – How did the experience change my understanding of the problem, people, or system? 🔹 My Progress – What has changed over a week, month, or a longer period? I have found that documenting creates a powerful repository of experiences. A daily reflection helps me capture the small but important observations. A weekly reflection helps me identify patterns and connect different experiences. A monthly reflection helps me step back and ask: What impact am I creating? What am I learning? Where am I growing? And what needs to change? In this sector, where our work often involves complex systems, communities, government stakeholders, field realities, and constantly evolving challenges, documentation becomes even more important. It helps us move from: Experience → Reflection → Learning → Action → Improvement And good documentation doesn’t have to be complicated. Some tools that can make it easier: 📝 Google Docs / Microsoft OneNote – for detailed notes, reflections and field observations 📊 Google Sheets / Excel– for tracking activities, data, progress and follow-ups 🗂️ Notion – for creating a structured personal knowledge and learning repository 📱 Google Keep / Notes – for capturing quick observations and ideas on the go 🎨 Canva – for converting learnings and field insights into visually engaging stories 🧠 Miro – for mapping ideas, reflections, stakeholder journeys and systems 🤖 AI tools– for organising raw notes, identifying patterns, summarising reflections and turning observations into structured documentation For me, documentation has gradually evolved from “writing what I did” to “understanding why I did it, what happened, what I learned, and what I can do better next time.” And perhaps that is the real power. Your work creates experiences. Your documentation creates learning. Your reflection creates growth. But we should also ask: Are we documenting the learning that can help us create better impact tomorrow? #DevelopmentSector #Documentation #Learning #Reflection #ProfessionalGrowth #SocialImpact #DevelopmentWork #FieldWork #ContinuousLearning #KnowledgeManagement Maninder Singh M S Mahala Gandhi Fellowship VIDYA. S. MISHRA #me

Medical students across seven Shanghai schools reported using AI tools about 5 times per week on average, with more than 90% using at least two platforms and over 60% using three or more. Mainstream models like DeepSeek, Doubao, and ChatGPT dominate, with students deploying them for theoretical learning, exam question analysis, information retrieval, literature interpretation, and research design. Despite this heavy use, only about one-fifth reported access to a dedicated institutional AI education platform, and satisfaction with those platforms was mixed, underscoring a gap between organic student demand and formal educational infrastructure. Key takeaways - AI adoption is high and multipolar: 94% use DeepSeek, 58% Doubao, 65% ChatGPT, and 91% use 2+ platforms, reflecting a “tool stack” approach rather than reliance on a single model. - Usage patterns differ by stage and role: undergrads lean on AI for exam prep, while master’s and doctoral students use it more for research tasks like study design and data analysis; part-time students show especially strong needs in research and exam analysis due to work–study pressures. - Disciplines shape use cases: clinical and nursing students rely more on AI for question analysis, whereas basic medicine, public health, and pharmacy students emphasize research support; traditional Chinese medicine students have lower demand for literature translation and interpretation. - Only 20% reported an institutional AI education platform, and satisfaction among users was modest (mean ~72/100, with ~21% dissatisfied), with no strong predictors of satisfaction—suggesting current platforms are generic rather than tightly aligned with task–technology fit. - Students overwhelmingly expect future platforms to go beyond generic chatbots toward tailored functions such as literature translation and interpretation, exam analysis, clinical trial support, basic lab assistance, knowledge mapping, frontier knowledge curation, virtual simulation, and even intelligent emotional support, with priorities varying by discipline and educational stage. Dipu’s Take Medical schools are no longer deciding whether students will use AI; they are deciding whether that use will be intentional, equitable, and well-governed. The signal here is clear: students want institutionally supported, discipline-aware, and stage-specific platforms that integrate AI into core workflows. The real opportunity is to treat AI platforms as part of the educational architecture that is co-designed with students, differentiated for undergrads vs. postgrads and clinical vs. basic sciences, and evaluated on how well they actually fit tasks like exam prep, research, and skills training, rather than as a one-size-fits-all pilot project.