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SEE both:
https://scott-macleod.
-https://
How best to dev #WUaSunivs' #AGIwuAs #ArtificalGeneralIntelligence, I asked https://aistudio.google.com & in Wikipedia's 342 langs w #WikidataWUaS as #WUaSBackendStructuredDatabase w #AIandML #LLMs? https://scott-macleod.
SEE both:https://t.co/61HLutrpPD
— WorldUnivandSch (@WorldUnivAndSch) May 28, 2025
-https://t.co/o7Bge23ug2
How best to dev #WUaSunivs' #AGIwuAs #ArtificalGeneralIntelligence, I asked https://t.co/XIW30icb9y & in Wikipedia's 342 langs w #WikidataWUaS as #WUaSBackendStructuredDatabase w #AIandML #LLMs? https://t.co/CW2azsKXu6 ~
https://x.com/WorldUnivAndSch/
https://x.com/Q_YogaMacFlower/
https://x.com/scottmacleod/
https://x.com/sgkmacleod/
https://x.com/WUaSPress/
https://x.com/HarbinBook/
https://x.com/TheOpenBand/
https://www.linkedin.com/
https://www.linkedin.com/
https://www.linkedin.com/
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SEE both-
https://scott-macleod.blogspot.com/2025/05/redflower-currant-ribes-sanguineum.html
https://worlduniversityandschool.blogspot.com/2025/05/asked-google-ai-studio.html
How best to dev #WUaSunivs' #AGIwuAs #ArtificalGeneralIntelligence & with what #AItools? https://t.co/J0sWA3gPth or https://t.co/85Ng4pZdbx? How to dev in Wikipedia's 342 langs w #WikidataWUaS, as #WUaSBackendStructuredDatabase too & w #AIandML #LLMs? - https://t.co/CW2azsKXu6 ~
— WorldUnivandSch (@WorldUnivAndSch) May 28, 2025
https://x.com/WorldUnivAndSch/
https://x.com/Q_YogaMacFlower/
https://x.com/scottmacleod/
https://x.com/sgkmacleod/
https://x.com/WUaSPress/
https://x.com/HarbinBook/
https://x.com/TheOpenBand/
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Asked Google AI Studio - https://aistudio.google.com/ - on W May 28, 2025 -
How best to develop MIT OCW-centric wiki World University and School's ~200 online universities (in ~200 countries' and in their main languages) #WUaSunivs' #AGIwuAs Artificial General Intelligence #
Eg from here - https://wiki.
http://
And with also -
https://en.wikipedia.org/wiki/
Available in 342 languages)
https://www.wikidata.org/wiki/
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This is an incredibly ambitious and potentially transformative vision! Developing ~200 MIT OCW-centric online universities (WUaSunivs) across different countries and languages, powered by AI and structured with Wikidata, is a monumental task. Here's a breakdown of how you might approach this, integrating the tools and platforms you've mentioned:
MIT OCW as Foundation: Stick to the high-quality, open-licensed content from MIT OCW as the starting point. Wiki Spirit: Embrace collaborative, open editing, and community building. Structured Data First (Wikidata): Build a robust backend data model before (or in parallel with) extensive frontend wiki development. This makes content manageable, queryable, and scalable. AI as Augmentation, Not Replacement (Initially): Use AI to assist humans in translation, content creation, organization, and personalization, but always with human oversight, especially in early stages. Iterative Development: Start small, test, learn, and then scale. Don't try to boil the ocean. Localization, Not Just Translation: Content needs to be culturally relevant, not just linguistically accurate. AI can help, but human experts are crucial. Community Driven: Success depends on building active communities in each language/country.
Establish Core #WikidataWUaS Structure: Define Entities: What are the core "things" you need to describe? Course (e.g., MIT's 6.001) Module/Lecture Learning Resource (video, PDF, interactive simulation) Learning Objective Concept/Topic Translation (linking original to translated resources) WUaS University Instance (e.g., WUaS Brazil) Language
Define Properties: How do these entities relate? has part (Course has Modules) based on (WUaS Course based on MIT OCW Course) language of work or name URL of resource translation of main subject prerequisite
Initial Data Population: Manually (or with scripts) populate Wikidata with core MIT OCW courses. Link to existing MIT OCW resources.
Leverage This is a GOLDMINE. Systematically ingest the metadata and links from these already translated courses into your #WikidataWUaS. Prioritize languages available here for your pilot phase.
AI-Powered Translation Augmentation (Gemini/AI Studio & other LLMs): Tool Choice: Google's Gemini (via AI Studio or API) is excellent for many tasks. Consider also other models specializing in translation if needed (e.g., Meta's NLLB, DeepL for quality checks). Workflow: For courses not yet translated, use LLMs to generate initial translations of: Course titles, descriptions, syllabi Lecture notes, subtitles for videos (if transcripts exist)
Crucial: These translations MUST be reviewed and edited by human speakers of the target language, ideally with subject matter expertise. AI is the first pass. Store translation metadata (source, tool used, reviewer) in #WikidataWUaS.
Develop Pilot WUaS Wiki Instances: Set up basic wiki structures (e.g., pt.wiki.worlduniversityandschool.org for Portuguese/Brazil). Create templates for course pages that can potentially pull data from #WikidataWUaS (using Lua modules and Wikidata integration if your wiki software supports it, like MediaWiki). If not, manual creation based on Wikidata data is the start. Focus on presenting the translated MIT OCW content clearly.
Community Building (Pilot Languages): Reach out to educators, students, and open-knowledge advocates in the target countries/languages. Recruit volunteers for translation review, content localization, and community moderation.
Expand Language Coverage Systematically: Prioritize based on demand, volunteer availability, and existing translated resources. Continue the AI-assisted translation + human review workflow.
AI-Assisted Content Generation (with Human Oversight): Gemini/AI Studio Use Cases: Summaries: Generate summaries of lectures or readings. FAQs: Create FAQs based on course content. Study Guides/Outlines: Draft initial study guides. Quiz Questions (Multiple Choice, Short Answer): Generate draft questions. Explanations of Complex Concepts: Ask the LLM to explain a concept in simpler terms or with analogies, then refine.
Store these AI-generated (and human-refined) materials, linking them to the relevant courses in #WikidataWUaS and on the wikis.
Integrate #AIandML and #LLMs into the Curriculum Itself: Develop introductory courses about AI, ML, and LLMs, using AI tools to help create the content. Showcase how AI tools (like Gemini) can be used as learning aids.
Develop #WUaSBackendStructuredDatabase (Beyond Wikidata): While Wikidata is great for public, linked open data, you might need a private backend for: User accounts (if you offer progression tracking, certificates - though this moves away from pure OCW). Detailed learning analytics. Managing AI tool usage and API keys.
This could be a traditional relational database or a NoSQL solution, depending on needs.
Deeper AI Integration: Personalized Learning Paths: AI analyzes a student's goals and current knowledge (perhaps via assessments) to recommend a sequence of courses or modules. This requires significant data and sophisticated algorithms. AI Tutors/Chatbots: Develop LLM-powered chatbots (using Gemini or similar) for each course or subject area to answer student questions, provide hints, and offer explanations. Fine-tune models on your specific course content. Automated Assessment & Feedback (for formative assessment): AI can help grade simple assignments or provide initial feedback on essays, but human oversight for summative assessment is key.
Exploring #AGIwuAs (Artificial General Intelligence): This is the most ambitious part. WUaS itself could become a platform for AGI research focused on education. How WUaS can contribute: Data Generation: The interactions of millions of learners with diverse content across languages could provide unique datasets for training and understanding learning processes. Testbed for AGI in Education: If/when AGI capabilities emerge, WUaS could be a prime environment to test how AGI can: Create entirely new, personalized curricula in real-time. Adapt teaching methods dynamically to individual cognitive styles. Foster deep understanding and creativity beyond rote learning.
The development of the AI tools for WUaS (translation, content generation, tutoring) serves as stepping stones and research into components that might one day contribute to AGI.
Not for hosting WUaS courses. Wikipedia is an encyclopedia, not a learning platform or course repository. How to use it: Create a well-sourced Wikipedia article about World University and School itself (if it meets notability guidelines). As individual WUaS country/language instances become significant and well-established, they might warrant their own Wikipedia articles (again, subject to notability). Link from WUaS wiki pages to relevant Wikipedia articles for background information on subjects. Use Wikidata to manage interlanguage links for any WUaS-related Wikipedia pages.
Wiki Platform: MediaWiki (powers Wikipedia) is the obvious choice for its scalability, multilingual features, and Wikidata integration capabilities (via Wikibase extension or direct API calls). #WikidataWUaS: Run your own Wikibase instance or heavily use the public Wikidata. Using public Wikidata increases visibility and linked data benefits but gives less control. A hybrid approach is possible. AI Tools: Google AI Studio/Gemini API: Excellent for prototyping and deploying LLM features (translation, summarization, Q&A, content generation). Open-source LLMs: Consider self-hosting models like Llama, Mistral for more control or cost savings at scale, but this requires significant technical expertise. Translation APIs: Google Translate API, DeepL API, etc.
Backend Database (if needed beyond Wikibase): PostgreSQL, MySQL, MongoDB, etc. Programming Languages for Scripting & Integration: Python is dominant in AI/ML and data processing. JavaScript for frontend wiki enhancements. PHP for MediaWiki extensions.
Scale & Complexity: Start small, iterate, automate as much as possible. Translation Quality: Human review is non-negotiable. Build communities of translators/reviewers. Content Consistency: #WikidataWUaS is key. Use templates on wikis. Community Engagement: This is a social challenge as much as a technical one. Active outreach, clear contribution guidelines, and recognition are vital. Funding & Sustainability: Open source and volunteer-driven is great, but infrastructure, core team, and specialized AI development may require funding. Digital Divide: Ensure content is accessible on low-bandwidth connections and diverse devices.
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https://en.wikipedia.org/wiki/Ribes_sanguineum
https://commons.wikimedia.org/wiki/Ribes_sanguineum
...
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