Microsoft Learn AI citations

Here are some top AI learning and development platforms: 1. {{[[Coursera]]:https://www.coursera.org}} : Offers a wide range of AI courses from top universities and companies, including machine learning and deep learning specializations. 2. {{[[edX]]:https://www.edx.org}} : Provides access to AI courses from institutions like MIT and Harvard, focusing on various aspects of artificial intelligence. 3. {{[[Udacity]]:https://www.udacity.com}} : Known for its Nanodegree programs in AI and machine learning, featuring hands-on projects and mentorship. 4. {{[[LinkedIn Learning]]:https://www.linkedin.com/learning}} : Offers numerous AI-related courses that cater to professionals looking to enhance their skills in data science and machine learning. 5. {{[[DataCamp]]:https://www.datacamp.com}} : Focuses on data science and analytics, providing interactive courses on AI, machine learning, and data manipulation. 6. {{[[Kaggle]]:https://www.kaggle.com}} : A platform for data science competitions that also offers datasets and tutorials for learning AI and machine learning. 7. {{[[Fast.ai]]:https://www.fast.ai}} : Provides free courses aimed at making deep learning accessible, emphasizing practical applications and coding. 8. {{[[Google AI]]:https://ai.google/education}} : Offers resources and courses on AI, including TensorFlow and machine learning best practices. 9. {{[[Microsoft Learn]]:https://learn.microsoft.com}} : Provides learning paths and modules focused on AI and machine learning using Microsoft tools and Azure. 10. {{[[IBM Skills Academy]]:https://www.ibm.com/training}} : Offers courses and certifications in AI, data science, and machine learning, leveraging IBM's technology and expertise.

Top AI learning and development platforms in 2025 include: - {{[[Absorb LMS]]:https://www.absorblms.com/blog/top-ai-learning-platforms/}}: Cloud-based platform with AI-driven personalized learning paths, skills assessment, and workflow automation. - {{[[Docebo]]:https://www.absorblms.com/blog/top-ai-learning-platforms/}}: Uses AI for personalized content delivery, auto-tagging, and enhanced search capabilities. - {{[[360Learning]]:https://www.absorblms.com/blog/top-ai-learning-platforms/}}: Combines LMS, LXP, and academy features with AI-powered authoring, quiz generation, and content transcription. - {{[[D2L Brightspace]]:https://www.d2l.com/blog/ai-learning-platforms/}}: Enterprise LMS with AI course builder, intelligent agents, predictive analytics, and strong compliance support. - {{[[LearnUpon]]:https://www.workramp.com/blog/11-best-ai-learning-platforms}}: Features interactive bots, AI coaching, exam builders, and automated content recommendations. - {{[[WorkRamp]]:https://www.workramp.com/blog/11-best-ai-learning-platforms}}: Integrates generative AI for content creation, role-playing bots, and customizable learning paths. - {{[[Coursera for Business]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}}: Offers a vast course catalog with AI-driven recommendations for scalable credentialed learning. - {{[[LinkedIn Learning]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}}: Focuses on professional skill development with AI-personalized learning paths linked to career goals. - {{[[ChatGPT]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}} and {{[[Claude]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}}: Generative AI assistants providing on-demand learning support, content summarization, and tutoring. These platforms emphasize personalized learning, AI-assisted content creation, automated administration, and scalable upskilling/reskilling solutions suitable for enterprises and individual learners alike[1][2][3][4].

Top AI Learning and Development Platforms: 1. {{[[Coursera]]:https://www.coursera.org}} : Offers comprehensive AI and machine learning courses from top universities and tech companies. 2. {{[[Udacity]]:https://www.udacity.com}} : Provides specialized nanodegree programs in AI, machine learning, and deep learning. 3. {{[[edX]]:https://www.edx.org}} : Features AI and data science courses from prestigious institutions like MIT and Harvard. 4. {{[[DataCamp]]:https://www.datacamp.com}} : Focuses on interactive Python and R programming for data science and AI. 5. {{[[Google AI]]:https://ai.google/education}} : Offers free machine learning and AI educational resources and tools. 6. {{[[IBM Learning]]:https://www.ibm.com/training}} : Provides enterprise-level AI and machine learning training programs. 7. {{[[Kaggle]]:https://www.kaggle.com}} : Offers free AI and machine learning courses with practical coding challenges. 8. {{[[LinkedIn Learning]]:https://www.linkedin.com/learning}} : Provides video-based AI and technology skill development courses. 9. {{[[Pluralsight]]:https://www.pluralsight.com}} : Offers technology skills training with extensive AI and machine learning content. 10. {{[[Microsoft Learn]]:https://learn.microsoft.com}} : Provides free AI and machine learning learning paths and certifications. These platforms cover various skill levels from beginner to advanced, offering both theoretical knowledge and practical implementation skills in AI and machine learning.

Here’s a compact set of **Generative-UI design patterns** that work well in domain/enterprise tools, with solid references for each: * **Ask → Edit → Agent (mode switching)**: start with Q\&A, escalate to draft edits, then let an agent execute bounded tasks; always expose scope & rollback. ([The GitHub Blog][1]) * **Sidecar copilot (in-app focus)**: persistent right/left pane that stays in the user’s workflow with quick actions, context chips, and safe “apply” controls. ([Microsoft Learn][2]) * **Structured prompt builders**: form-like inputs, parameters, and constraints instead of raw free text for repeatable, auditable outcomes. ([pair.withgoogle.com][3]) * **Plan → Preview → Apply (diff-first)**: show a plan and a visual diff before changes; require explicit confirmation and provide undo/versioning. ([UX Collective][4]) * **Outcome-oriented canvases**: users state intent; UI composes the steps/components and adapts layout dynamically to reach the goal. ([Nielsen Norman Group][5]) * **Tuners & Governors**: adjustable sliders/switches to steer style/strictness and hard guardrails for safety (scope, data boundaries). ([shapeof.ai][6]) * **Confidence & provenance affordances**: show “draft” labels, uncertainty/confidence hints, and citations/traceback for generated results. ([pair.withgoogle.com][3], [Nielsen Norman Group][7]) * **Inline suggestions**: non-modal, inline proposals (e.g., code/text/table rows) that are easy to accept/modify/reject. ([UX Collective][4]) * **Error-aware recovery**: obvious escape hatches—revert, manual override, escalate to human, and targeted re-prompts. ([UX Collective][4]) * **Domain tool integration (actions & tools)**: expose verbed buttons the agent can call (simulate first, then run) with scoping and audit logs. ([Microsoft Learn][8]) * **Copilot UX principles (human-in-control, no anthropomorphism)**: set expectations, show limits, and keep the human as pilot. ([Microsoft Learn][2]) * **Generative UI primers & exemplars**: roundups and visual catalogs to inform component choices across enterprise contexts. ([UX Planet][9], [Medium][10]) * **Domain analytics copilots (pattern transfer)**: apply the same patterns to BI/analytics UIs (query → draft insight → refine → apply). ([Microsoft Learn][11]) * **Cloud app UX for gen-AI**: Google’s guidance on task framing, handoffs to deterministic flows, and guardrails for agents. ([Google Cloud][12]) ## References (handy starting points) * NN/g: *Generative UI & Outcome-Oriented Design*. ([Nielsen Norman Group][5]) * Microsoft: *Copilot UX guidance (ISV)* and *Copilot Studio agent guidance*. ([Microsoft Learn][2]) * Google PAIR: *People + AI Guidebook*. ([pair.withgoogle.com][3]) * GitHub: *Copilot Ask, Edit, Agent modes*. ([The GitHub Blog][1]) * Whitespectre/UX catalogs: *Emerging Gen-AI UI patterns*. ([Medium][10]) * UX Planet: *Generative-AI UX design patterns* (system/scope/spatial lenses). ([UX Planet][9]) * Designing for Analytics (enterprise LLM UX considerations). ([designingforanalytics.com][13]) * Shape of AI: pattern taxonomy (Wayfinders, Tuners, Governors, Trust builders). ([shapeof.ai][6]) * Google Cloud: *UX considerations for gen-AI apps* + *101 blueprints* (design + architecture). ([Google Cloud][12]) * Microsoft Power BI Copilot (analytics copilot UI). ([Microsoft Learn][11]) Want me to turn these into a checklist you can drop into your design system (with component examples)? [1]: https://github.blog/ai-and-ml/github-copilot/copilot-ask-edit-and-agent-modes-what-they-do-and-when-to-use-them/?utm_source=chatgpt.com "Copilot ask, edit, and agent modes: What they do and ..." [2]: https://learn.microsoft.com/en-us/microsoft-cloud/dev/copilot/isv/ux-guidance?utm_source=chatgpt.com "Creating a dynamic UX: guidance for generative AI ..." [3]: https://pair.withgoogle.com/guidebook/?utm_source=chatgpt.com "People + AI Guidebook - Home" [4]: https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1?utm_source=chatgpt.com "20+ GenAI UX patterns, examples and implementation tactics" [5]: https://www.nngroup.com/articles/generative-ui/?utm_source=chatgpt.com "Generative UI and Outcome-Oriented Design" [6]: https://www.shapeof.ai/?utm_source=chatgpt.com "The Shape of AI | UX Patterns for Artificial Intelligence Design" [7]: https://www.nngroup.com/articles/genai-ux-research-agenda/?utm_source=chatgpt.com "A Research Agenda for Generative AI in UX" [8]: https://learn.microsoft.com/en-us/microsoft-copilot-studio/guidance/?utm_source=chatgpt.com "Microsoft Copilot Studio guidance documentation" [9]: https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99?utm_source=chatgpt.com "Generative AI UX Design Patterns" [10]: https://medium.com/whitespectre/emerging-ui-ux-patterns-in-generative-ai-a-visual-guide-74000c7198a4?utm_source=chatgpt.com "Emerging UI/UX Patterns in Generative AI - Medium" [11]: https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction?utm_source=chatgpt.com "Overview of Copilot for Power BI" [12]: https://cloud.google.com/blog/products/ai-machine-learning/how-to-build-a-genai-application?utm_source=chatgpt.com "UX considerations for generative AI apps and agents" [13]: https://designingforanalytics.com/resources/ui-ux-design-for-enterprise-llms-use-cases-and-considerations-for-data-and-product-leaders-in-2024-part-1/?utm_source=chatgpt.com "UI/UX Design Considerations for LLMs in Enterprise ..."

The most common **design patterns** for Generative UI in domain tools center on dynamic UI construction, adaptation to user context, seamless integration with design systems, and maintaining interactive and accessible experiences.[1][2][3][4] ## Key Generative UI Patterns - **Pattern-Based Generation:** Define a library of standardized, atomic components (e.g. buttons, tables, forms) which the generative AI combines per user scenario, ensuring consistency and theme alignment.[2][3] - **Prompt-to-UI Mapping:** Use structured prompts or intent models to translate user descriptions or requirements into structured UI definitions, often as JSON or similar representations that map directly to design system components.[3][2] - **Live Contextual Adaptation:** UIs that adapt in real time to changing user context, data, or workflow requirements—such as resizing, adjusting compositional layouts, or changing fields based on current task flow.[4][1] - **Outcome-Oriented Design:** Rather than focusing on static templates, generative UIs aim for task, workflow, or outcome optimization—surfacing controls and data most relevant for the user’s stated or inferred goals.[5][4] - **Validation and Safety Guardrails:** Implement rigorous testing and output validation, such as capping array/table sizes or checking that the generative model’s output adheres to allowed types, to ensure UI stability and prevent runtime errors.[3] - **Seamless Design System Integration:** Align all generated UI elements to a design system’s color, spacing, and typography tokens so they feel native and maintain branding and accessibility standards.[2][3] ## Best Practices - Build with a **dedicated design system** for Generative UI (like Thesys "Crayon"), emphasizing accessibility, interactivity, and responsive layouts.[2] - Use **iterative QA and user feedback** cycles, starting with pilot projects before broad rollouts, and always log AI outputs for ongoing improvement.[3] - Treat all AI-generated component specifications as production code, requiring validation, error handling, and secure sandboxing.[4][3] ## Further Reading and References - [20+ GenAI UX Patterns, Examples, and Implementation Tactics (UXDesign)](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1)[1] - [Technical Architecture: First Generative UI API (Thesys)](https://www.thesys.dev/blogs/generative-ui-architecture)[2] - [Enterprise Readiness for Generative UI (Thesys)](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build)[3] - [Generative UI Guide 2025: 15 Best Practices & Examples (Mockplus)](https://www.mockplus.com/blog/post/generative-ui)[4] - [Generative UI and Outcome-Oriented Design (NN/G Nielsen Norman Group)](https://www.nngroup.com/articles/generative-ui/)[5] These sources offer both tactical implementation advice and strategic frameworks for leveraging generative UIs in business and enterprise software contexts.[1][5][4][2][3] [1](https://uxdesign.cc/20-genai-ux-patterns-examples-and-implementation-tactics-5b1868b7d4a1) [2](https://www.thesys.dev/blogs/generative-ui-architecture) [3](https://www.thesys.dev/blogs/getting-enterprise-ready-for-generative-ui-what-to-know-before-you-build) [4](https://www.mockplus.com/blog/post/generative-ui) [5](https://www.nngroup.com/articles/generative-ui/) [6](https://dl.acm.org/doi/10.1145/3715336.3735780) [7](https://www.mockplus.com/blog/post/generative-ai-tools) [8](https://arxiv.org/html/2505.15049v1) [9](https://www.shapeof.ai) [10](https://www.elsewhen.com/reports/from-generative-ai-to-generative-ui/) [11](https://uxplanet.org/generative-ai-ux-design-patterns-192bb169ab99) [12](https://www.nngroup.com/articles/genai-ux-research-agenda/) [13](https://martinfowler.com/articles/gen-ai-patterns/) [14](https://www.coveo.com/blog/generative-ai-best-practices/) [15](https://www.thesys.dev/blogs/how-generative-ui-is-transforming-internal-tools-across-the-enterprise) [16](https://www.getaprototype.com/knowledge-base/generative-ui-design) [17](https://www.k2view.com/blog/what-is-a-best-practice-when-using-generative-ai/) [18](https://uxdesign.cc/designing-for-ai-engineers-what-ui-patterns-and-principles-you-need-to-know-8b16a5b62a61) [19](https://devpulse.io/insights/ux-ui-design-best-practices-2025-enterprise-applications/) [20](https://uxplanet.org/7-key-design-patterns-for-ai-interfaces-893ab96988f6)

I'll search for information about design patterns for Generative UI in domain-specific tools.Let me search for more specific information about design patterns for generative UI in domain-specific applications.Based on my search, here are the key design patterns for Generative UI in domain tools: ## Core Design Patterns for Generative UI ### 1. **Outcome-Oriented Design Pattern** In the future, generative UI will dynamically create customized user interfaces in real-time. This shift will force an outcome-oriented design approach where designers prioritize user goals and define constraints (for AI to operate within), rather than design discrete interface elements. ### 2. **Constraint-Based Generation Pattern** Rather than designing specific UI elements, designers focus on: - Defining user goals and objectives - Setting operational constraints for AI systems - Allowing dynamic interface creation within those boundaries ### 3. **Iterative Refinement Pattern** Modern generative UI tools support iterate easily on your designs and edit your generated screens in seconds, enabling rapid prototyping and refinement cycles. ### 4. **Multi-Modal Generation Pattern** Image generation tools for new and visually appealing images based on existing patterns or styles. Sound generation tools for melodies, harmonies and even entire compositions - supporting comprehensive multimedia interface generation. ## Key Implementation Strategies ### Real-Time Adaptation The goal of these patterns is to help mitigate and overcome some of the challenges with generative AI implementations such as cost, latency and hallucinations. ### Enterprise Integration With the latest 2024 Q2 release, Progress brings generative AI integration, enhanced design system capabilities and accessible data visualization. ## Key Resources and Tools **Research and Guidelines:** - [Nielsen Norman Group - Generative UI and Outcome-Oriented Design](https://www.nngroup.com/articles/generative-ui/) - [ACM CHI 2024 - Design Principles for Generative AI Applications](https://dl.acm.org/doi/10.1145/3613904.3642466) - [Towards Data Science - Generative AI Design Patterns Guide](https://towardsdatascience.com/generative-ai-design-patterns-a-comprehensive-guide-41425a40d7d0/) **Tools and Platforms:** - [UX Pilot - AI UI Generator](https://uxpilot.ai/) - [Telerik/Progress - Enterprise AI Integration](https://www.telerik.com/blogs/telerik-kendo-ui-2024-q2-release-here-see-whats-new) - [Merge - AI Design Tools for 2025](https://merge.rocks/blog/top-ai-design-tools-for-ux-ui-designers-in-2025) **Industry Analysis:** - [Mockplus - 22 Best Generative AI Tools for UI/UX](https://www.mockplus.com/blog/post/generative-ai-tools) - [UXPin - UI/UX Design Trends 2025](https://www.uxpin.com/studio/blog/ui-ux-design-trends/) The trend shows a shift from traditional component-based design to constraint-based, outcome-oriented approaches where AI generates interfaces dynamically based on user goals and contextual requirements.

Here are some top AI skills development platforms: 1. {{[[Coursera]]:https://www.coursera.org}} : Offers a wide range of AI courses from universities and companies, including machine learning and deep learning specializations. 2. {{[[edX]]:https://www.edx.org}} : Provides access to AI courses from top institutions, focusing on both foundational and advanced topics in artificial intelligence. 3. {{[[Udacity]]:https://www.udacity.com}} : Known for its Nanodegree programs in AI and machine learning, emphasizing hands-on projects and real-world applications. 4. {{[[LinkedIn Learning]]:https://www.linkedin.com/learning}} : Features numerous courses on AI, machine learning, and data science, tailored for professionals looking to enhance their skills. 5. {{[[Kaggle]]:https://www.kaggle.com}} : A platform for data science competitions that also offers datasets and notebooks for practicing AI and machine learning skills. 6. {{[[DataCamp]]:https://www.datacamp.com}} : Focuses on data science and analytics, with interactive courses on Python, R, and machine learning. 7. {{[[Fast.ai]]:https://www.fast.ai}} : Provides free courses aimed at making deep learning more accessible, with a focus on practical applications. 8. {{[[Pluralsight]]:https://www.pluralsight.com}} : Offers a variety of tech courses, including AI and machine learning, with a focus on skill assessments and paths. 9. {{[[Google AI]]:https://ai.google/education}} : Features resources and courses from Google, aimed at helping learners understand AI concepts and applications. 10. {{[[Microsoft Learn]]:https://learn.microsoft.com}} : Provides learning paths and modules on AI and machine learning, integrating Microsoft technologies and tools. These platforms cater to various learning styles and levels, making them suitable for anyone interested in developing AI skills.

Top AI skills development platforms in 2025 include: {{[[Coursera]]:https://www.nucamp.co/blog/ai-essentials-for-work-2025-top-10-ai-tools-to-learn-in-2025-for-career-growth}}: Offers over 300 AI courses from leading institutions like Google, IBM, and DeepLearning.AI, with flexible online programs ranging from beginner to advanced AI topics, including generative AI and prompt engineering. {{[[EdCast by Cornerstone]]:https://www.absorblms.com/blog/top-ai-learning-platforms/}}: An AI-powered Learning Experience Platform (LXP) delivering personalized learning and career mobility through its SkillsDNA™ framework and real-time guidance tools. {{[[TalentLMS]]:https://www.absorblms.com/blog/top-ai-learning-platforms/}}: Provides AI-driven course creation with TalentCraft, including grammar correction, tone adjustment, and AI-assisted skills mapping. {{[[Degreed]]:https://www.absorblms.com/blog/top-ai-learning-platforms/}}: Uses AI coaching via Degreed Maestro for personalized skill assessment, career development, and leadership training. {{[[Sana Labs]]:https://www.absorblms.com/blog/top-ai-learning-platforms/}}: Combines LMS and LXP features with AI tutors, personal coaches, and AI-driven learning analytics dashboards. {{[[Docebo]]:https://sanalabs.com/learn-blog/ai-learning-platforms-2025}}: Known for AI-powered Deep Search, auto-tagging, and personalized learning recommendations enhancing content discoverability and learner engagement. {{[[Cornerstone]]:https://sanalabs.com/learn-blog/ai-learning-platforms-2025}}: Features the Cornerstone Skills Graph for AI-driven skill-to-role matching, personalized learning paths, and extensive content libraries for strategic workforce development. {{[[LinkedIn Learning]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}}: Delivers AI-generated personalized learning paths tied to professional profiles and career goals, with social learning features. {{[[ClearCompany Learning]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}}: Offers an all-around AI L&D platform integrating smarter training management with measurable impact. {{[[ChatGPT]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}} and {{[[Claude]]:https://blog.clearcompany.com/best-ai-learning-and-development-tools}}: Generative AI assistants providing on-demand learning support, virtual tutoring, and content generation. These platforms leverage AI to personalize learning, automate content creation, map skills to roles, and support career development across various industries and proficiency levels[1][2][3][4][7].

Top AI Skills Development Platforms: 1. {{[[Coursera]]:https://www.coursera.org}} : Offers comprehensive AI and machine learning courses from top universities like Stanford and Google. 2. {{[[Udacity]]:https://www.udacity.com}} : Provides specialized nanodegrees in AI, machine learning, and deep learning with industry-focused curriculum. 3. {{[[edX]]:https://www.edx.org}} : Features AI and data science programs from MIT, Harvard, and other prestigious institutions. 4. {{[[DataCamp]]:https://www.datacamp.com}} : Focuses on interactive coding platforms for Python, R, and machine learning skills. 5. {{[[Kaggle]]:https://www.kaggle.com}} : Offers free AI and machine learning competitions, datasets, and hands-on coding environments. 6. {{[[fast.ai]]:https://www.fast.ai}} : Provides free, practical deep learning courses with a focus on practical implementation. 7. {{[[Google AI]]:https://ai.google/education}} : Offers free AI learning resources, tutorials, and certification programs. 8. {{[[IBM Skills]]:https://www.ibm.com/skills}} : Provides AI and machine learning learning paths with practical certifications. 9. {{[[LinkedIn Learning]]:https://www.linkedin.com/learning}} : Hosts numerous AI and machine learning video courses from industry experts. 10. {{[[Pluralsight]]:https://www.pluralsight.com}} : Offers advanced technical skills training in AI and machine learning technologies.