Study and Learn Agent: Now in 50 Languages, images improvements, ready when you open Copilot
When we brought Study and Learn Agent to general availability, the promise was simple: the student does the thinking. Study and Learn asks before it tells. It pushes you to try. It makes learning interactive and quizzes you on what you missed. It lives at aka.ms/studyandlearn inside your Microsoft Education tenant. It coaches learners across any subject; explaining concepts, walking through problems step by step, supporting writing without doing the writing, and generating flashcards, quizzes, matching, and fill-in-the-blank activities from a learner's own materials. The design principle underneath all of it: preserve the productive effort that makes learning stick. The learner does the thinking. The agent coaches the thinking. 👥 Who can use it Study and Learn is available and pinned by default for every adult learner in a Microsoft Education tenant. No setup required — it's right there in the left navigation of Microsoft 365 Copilot. For K-12 students aged 13–17, there's one step first: Copilot Chat has to be enabled. Until it is, students in your tenant can't reach the agent. The good news is that it's a simple set of steps tutorial shows the exact enablement steps for IT admins. Watch the enablement walkthrough. 🎥 Full video tutorial If you want the full tour, Mike Tholfsen made an end-to-end tutorial that walks through every feature, start to finish. It's the fastest way to see the agent in action: Watch it here: ✨ Copilot Chat now opens with Study and Learn pre-selected For K-12 students who already have Copilot Chat enabled, Study and Learn is now pre-selected the moment they open Copilot Chat. The coach is right there, ready - no hunting through a menu, no wondering which agent to pick. Copilot Chat opens by default in the learning mode. A student who wants to work with something else can dismiss it in a tap. IT admins stay in control. The setting lives in the Education settings panel in the Microsoft 365 admin center. It's ON by default for K-12 and OFF by default for higher education, where admins can turn it on when they're ready. 🌍 Now available in 50 languages At GA, Study and Learn was optimized for English (US). Now it speaks the way your learners do. The agent is available in 50 languages beyond English. Set your browser or operating system to a supported locale and visit aka.ms/studyandlearn. The user experience and conversation are set in your language. The learning activities - flashcards, quizzes, matching, fill-in-the-blank - appear in your chosen app language. Additional language support is rolling out now and will be available to all users in the coming days. And you're not locked in. A learner can switch languages mid-conversation for a genuinely multilingual study session. Learning a new language and want the vocabulary words in the new language but the explanations in your native language? No problem. Want to switch languages to practice the vocabulary with Study and Learn, no problem, you can do that too. The coaching language follows you. For a student learning in their second language, or a family where the kitchen-table language isn't the classroom language this is the difference between a tool that talks at them and one that meets them where they are. 🖼️ Images that show, instead of just tell Some things are easier to see than to describe. A cell's structure. The layers of the atmosphere. Where the femur meets the hip. Study and Learn conversations can now include a carousel of relevant, web-safe images sourced through search, woven right into the flow of the agent's response. Each image supports the next step in a learner's understanding, a visual nudge toward the answer they're working out for themselves, without shortcutting the effort that gets them there. This is especially powerful for visual subjects like biology, geography, and chemistry, where a single well-placed image does what a paragraph can't. This experience will get better over time. It is rolling out now and will be available to all users in the coming days. You can always share feedback here. 💬 A prompt pack to help learners get started Here’s a prompt pack - a set of ready-to-use prompts that show learners exactly how to put the agent to work, organized around the moments they actually face: Understand a difficult topic — “Use these materials to find the most important ideas and help me understand them.”, “Explain [topic] step by step, then check my understanding.” Practice what you know — “Create a matching activity for the key terms in this chapter.”, “Make a fill-in-the-blanks activity from these materials.” Improve my work — “Review what I missed and explain the ideas I need to practice.”, “Help me understand why my answer was incorrect, then let me try again Work on an assignment — “Help me brainstorm ideas for this assignment.”, “Review my draft and help me find one area to strengthen.”, “Help me check whether my evidence supports my main point.” Get ready for a test — “Use these materials to identify the topics I should focus on.”, “Give me a five-minute review of these concepts I’m still struggling with.” Every prompt keeps the learner in the driver's seat — the agent helps them think and practice, never hands over the answer. Share the pack at the start of a unit and watch how it changes the way students engage. 📋 Coming soon: educators can assign the agent from Teams Everything so far puts the coach in the learner's hands. This next one puts it in the educator's. Rolling out soon, educators will be able to assign the use of Study and Learn Agent directly from a Teams assignment. Instead of a blanket "you can use this," you'll be able to point learners to the agent for a specific assignment built right into the assignment they're already opening. The through-line Every one of these (the languages, the pre-select, the prompt pack, the images, the assignments) meet the learner where they are. In their language. The moment they open Copilot. With the words to get started and the visuals to see it clearly. Pointed there, when it counts, by the educator who knows them. Open Microsoft 365 Copilot. Look in the left navigation. Then watch Mike's full tutorial and try it on the thing you're studying next. Educators can also access the full professional development course on Study and Learn complete with suggestions on introducing the agent to students. Try Study and Learn Agent · Microsoft Education license required. K-12 student access (ages 13–17) requires IT to enable Copilot Chat. Resources: Try Study and Learn in your tenant: https://aka.ms/studyandlearn Full tutorial on Study and Learn: https://aka.ms/studyandlearnvideo Professional development for educators: https://aka.ms/studyandlearnmodule Enable Copilot Chat: https://aka.ms/enablecopilotchatvideo Support documentation: https://aka.ms/studyandlearndocumentation Anoo Padte is Principal Product Manager for AI in Education at Microsoft.
Copilot sidebar answers quiz
Dear Microsoft, Please help. Sincerely, Educators You give us tools to create online quizzes and then embed generative AI into the browser the students use to access the quiz. I asked it to answer the questions and after some throat-clearing, it proceeded to answer all of the questions. The futility of quizzes is one thing, but do these sidebar AIs allow leakage of private information out of our tenant? The quiz is only accessible to members of a team, but the sidebar is using my personal copilot account. If I had been looking at student grades via the teams web interface and asked the copilot sidebar to provide some insight on performance, would that private information have breached the boundary of our tenant?Agentic Mentor: A Specification-Driven, Multi-Agent Learning Tool
Project Overview Rather than relying on the model to know when the student understands, we built a pipeline where progress itself is gated: a student cannot move forward until they can demonstrate they understood what was just built. And rather than depending on commercial, token-billed APIs to make this teaching possible, the system is designed to run on locally hosted or free-tier models, keeping it accessible to students and institutions alike. As our contribution to Microsoft, we've handed over the full public repository behind Agentic Mentor. Given how central token-based services have become to software development, we see real potential for this approach in initiatives like GitHub Education, where affordable, agentic tools for students matter. In this post, we'll walk through what motivated the project, how Agentic Mentor works under the hood, and what we learned putting it to the test on a real piece of university coursework. The Project Journey This project was completed over three months. The first few weeks were spent on background research and requirements elicitation. We reviewed the literature on AI-assisted learning, specification-driven development and prompt ambiguity, elicited functional and non-functional requirements from our client supervisor at Microsoft, Lee Stott, and our academic supervisors, and broke the work into components we could build and test independently. This told us what was realistically achievable in the time we had, and which features were core to the tool rather than desirable extras. Implementation was coordinated through GitHub, with the team meeting daily to keep parallel work in sync and weekly meetings with both our supervisors and our client to report progress and check we were still building the right thing. We worked this way because the shape of the tool was still settling once implementation began, and that frequency of contact meant a wrong assumption surfaced in days rather than weeks. The most consequential decision of the project came out of those conversations. Agentic Mentor had been proposed as an assessment tool, but the AI does not write the assessment brief, so it cannot be relied on to read it as the academic intended, and grading on top of that reading would carry the model's misunderstanding into a student's mark. We pivoted to a learning tool, and the design of the pipeline followed from there. The final month went on completing both interfaces and evaluating the system, first against SpecBench as a correctness check, then against a real master's-level coursework. Two problems surfaced. Our GPU infrastructure went offline, so we moved to free-tier cloud models and scheduled runs around quota resets. And one of the questions the system generated turned out to be subtly wrong, and we had read it and accepted it without question, which is the automation bias we had spent the project writing about. Technical Details Agentic Mentor is built as a multi-agent, specification-driven pipeline. It has four agents; Research, Ingestion, Mentoring, and Viva. Each agent was implemented using the Microsoft Agent Framework. We chose to give each phase its own dedicated agent so that every stage could be equipped with the specific tools its task required, rather than relying on one general-purpose agent to handle the whole assignment. The agents run linearly, with each one writing its output to disk and the orchestrator passing the resulting file paths to the next stage. This design lets a session pause and resume. An overview of Agentic Mentor's architecture is seen in the figure below. Research Agent. This stage grounds the pipeline in external context. It uses GitHub and arXiv MCP servers to gather relevant literature and existing implementations. MCP gave the agent a uniform interface to both sources, meaning further ones could be added later simply by connecting another server, without reworking the agent itself. Ingestion Agent. Built around GitHub SpecKit, an open-source toolkit for spec-driven development, this agent converts the assignment brief and research context into structured specification files. Rather than letting the model resolve ambiguities in the brief on its own, SpecKit's clarification step surfaces unclear points directly to the student, who must answer before the pipeline continues. This choice keeps the student engaged in design decisions rather than letting the model make them silently. Mentoring Agent. This is where the student and the model actually build the project together, phase by phase, with the agent writing code and explaining its reasoning as it goes. Progress is gated: the student cannot move to the next phase until every task is complete, and a short multiple-choice checkpoint has been passed. The checkpoint tests understanding of what was just built rather than simply advancing on request. Viva Agent. Once implementation is complete, this agent interviews the student. Answers are checked against key points prepared in advance, with feedback given after each response and a full transcript saved for later review. How Microsoft's tools shaped the project. The Microsoft Agent Framework was the backbone that made the multi-agent design practical: it let us build four functionally distinct agents that could each carry their own tools and responsibilities while still communicating cleanly through a shared orchestration layer. Together with GitHub SpecKit's structured approach to specification-driven development, these tools gave Agentic Mentor a technical foundation that would have been considerably harder to assemble from scratch, and were central to making the pipeline's phase-gated, specification-first design actually work in practice rather than remaining a concept on paper. Demo This demo video shows an end-to-end demonstration of the VS Code extension of Agentic Mentor. Results and outcomes We evaluated Agentic Mentor in two phases: a benchmark check for basic correctness, followed by a real master's-level coursework that better matched the system's intended use. Phase 1: Code Correctness We used SpecBench to evaluate Agentic Mentor’s code correctness. SpecBench is a benchmark of 30 systems-level programming tasks with pre-existing test suites. We ran six of them using a locally served Qwen3.6-27B model. Our results show that tasks with common structural logic scored well, such as json_parser achieving a 97.7% pass rate. However, more complex tasks performed worse. On crypto_primitives, hallucinations prevented a testable solution being produced at all. This result reflects the limits of a small local model such as Qwen3.6-27B. However, the success of some tasks show that smaller local models have promise. Phase 2: Student Understanding The second phase evaluated the system against a real academic assignment on Test-Driven Development, which the team had previously completed without Agentic Mentor. Claude Sonnet 5 was used to reduce hallucinations and improve reasoning. We combined our own assessment as students with an interview with Jens Krinke, the module leader who set and assessed the coursework. From our perspective, we found that Agentic Mentor made a large, loosely specified project easier to approach, and the questions at each stage required active recall which helped us understand the assignment better. It improved on our original attempt by constructing a synthetic repository with known test–production pairs as concrete acceptance criteria, linking test and production files by co-occurrence in commit history rather than naming conventions, and producing unit test coverage within a properly separated project structure. The assessment of its understanding was less favourable. Jens rated its comprehension as comparable to a typical student's, but no better: it treated commit history as capable of confirming the presence of TDD, when it can only reveal the degree of its absence, and it missed an implicit hint regarding commit size. Lessons Learned Building Agentic Mentor taught our team a great deal about coding agents, local models, and the practical challenges of applying AI in education. These lessons shaped both the tool itself and how we think about deploying AI in learning contexts. Knowing where the tool fits. One of our clearest takeaways was that Agentic Mentor works best on well-specified, undergraduate-level assignments rather than open-ended, research-style coursework. Our evaluation on SpecBench's json_parser task, where the tool achieved a 97.7% pass rate, showed just how effective it can be on concrete, well-bounded problems. Our Test-Driven Development case study showed the same tool struggling once the task demanded interpreting ambiguous or partially hidden objectives. Even so, our own experience using the tool showed genuine improvements in our learning outcomes and even surfaced implementation ideas we hadn't considered ourselves, which reinforced our belief in the promise of agentic learning tools when applied to the right kind of problem. Navigating the local model trade-off. Our extensive experimentation with locally hosted models taught us that there is a trade-off between accessibility and quality. Local models performed well on smaller, simpler tasks, but as task complexity grew, code correctness declined and hallucinations became more frequent. This was a valuable lesson in engineering trade-offs: a fully local, cost-free deployment is achievable in principle, but a genuinely effective one currently depends on access to larger, cloud-hosted models. Rethinking what the tool should be for. Perhaps our biggest shift in thinking came from an idea we abandoned. We originally envisioned Agentic Mentor as an assessment tool, capable of evaluating a student's understanding for official grading. Working through the implementation made clear why that vision doesn't hold up: an AI system that didn't generate the assessment brief itself cannot be guaranteed to interpret an educator's intent correctly. This is a limitation of natural language interpretation that is well documented in the literature we reviewed. Recognizing this early enough to change course was itself a valuable exercise in engineering judgment, and it led us to reposition Agentic Mentor as a learning tool focused on helping students engage with agentic AI, rather than an assessment tool that asks AI to make judgments it isn't equipped to make reliably. Implications for Educators Our experience building and evaluating Agentic Mentor highlighted three critical takeaways for educators looking to integrate AI into their classrooms. Watch out for automation bias. Our academic evaluation revealed that the AI agents occasionally held technical misconceptions, which raised up the risk of automation bias; one of the biggest risks in AI-assisted education. When an AI speaks with absolute confidence, it’s incredibly easy to believe it. If students trust the model's outputs without sufficient questioning, they bypass the critical thinking the coursework was meant to provoke. Educational AI needs built-in friction to force the AI to highlight its own uncertainties and encourage students to question the output. Be careful using AI as an Assessment tool. The discovery of the AI’s technical misconceptions also suggests caution using AI for grading or formal assessment. Because the model can misunderstand core concepts the exact same way a student might, it cannot reliably evaluate student comprehension. This also led us to pivot Agentic Mentor strictly to a learning tool. AI is an excellent tool for supporting student learning through phased tasks, but it is not currently reliable enough to act as an autonomous judge of a student's underlying understanding. Agentic Development is reshaping software engineering. As agentic development reshapes software engineering, educators must shift their focus from teaching students how to write code manually, to teaching them how to prompt coding agents. This includes using practices such as specification-driven development, which reduces AI errors in code by preventing ambiguities in natural language prompts. Future Development The limitations we encountered while building Agentic Mentor point to two clear directions for further development. Both would make the system a more reliable learning tool for students. Validation agent Agentic Mentor currently interprets a project with the same gaps and misconceptions a typical student might bring to it. Because the system presents its output with confidence, those gaps can be passed on unchallenged. We propose extending the pipeline with a validation agent. This component would be dedicated to interrogating the questions and conclusions the system produces. Its purpose would be to introduce a deliberate layer of hesitation, surfacing assumptions explicitly rather than allowing them to reach the student as established fact. The intended outcome is a reduction in automation bias, encouraging students to engage critically with the system's output rather than accepting it uncritically. Integration with academic platforms As long as the system's understanding of an assignment is constrained in the same ways a student's is, it cannot be relied upon to identify a task's critical elements. A future version could accept explicit input from educators, specifying those critical elements in advance along with guidance on how students should be directed through them. This would require Agentic Mentor to move beyond a standalone local tool and integrate with the institution's existing education platform. Such integration would give educators direct control over the system's behaviour, making it both more trustworthy and more productive as a learning aid. Conclusion Agentic Mentor was built to help students learn from their coding coursework rather than simply complete it, embedding the learning process into the structure of the system instead of relying on vibe coding. We found that a model cannot be trusted to be consistently correct. Running on smaller local models trades capability for accessibility. And measuring student understanding is difficult without a large user study. Agentic Mentor nonetheless represents a step towards integrating AI-assisted development into education while preserving student understanding. With adequate guardrails against automation bias, it can serve as an accessible tool that adapts the learning process to the changes AI has brought to software development. Call to Action The challenge of "one-click" AI code generation in education is here to stay, but with Agentic Mentor, we aim to keep students actively engaged in problem-solving rather than bypassing it. We invite you to explore our work and help us build the future of AI in education. Explore the Code: Visit the Agentic Mentor GitHub Repository Run It Your Way: Execute the orchestrator via the CLI (see the main README), or use our custom GUI by running the VS Code Extension (located in the agentic-mentor-extension folder). Tools: Our pipeline is built on the Microsoft Agent Framework for multi-agent routing and GitHub SpecKit to enforce strict Specification-Driven Development. Team Our team involved in developing this project included 6 members. All of us are Masters students at UCL, studying either Software Systems Engineering or Artificial Intelligence and Data Engineering. Mark Connor – Team Leader – Software Engineer GitHub URL: https://github.com/markjconnor LinkedIn URL: http://www.linkedin.com/in/mark-connor2003 Alexander Filippov – Software Engineer GitHub URL: https://github.com/ucabavf LinkedIn URL: https://www.linkedin.com/in/alexander-f-003a5721b Weeraya Hew – Software Engineer GitHub URL: https://github.com/tingwry LinkedIn URL: https://www.linkedin.com/in/weeraya-hew-924a19261 Tanishka Jaikrishnia – Software Engineer GitHub URL: https://github.com/tanishkajaikrishnia LinkedIn URL: https://www.linkedin.com/in/tanishka-jaikrishnia-96b652274/ Pranav Kannan – Software Engineer GitHub URL: https://github.com/pranavk295 LinkedIn URL: https://www.linkedin.com/in/pranav-kannan-0b2a11221 Gabriel Mardakhaev – Software Engineer GitHub URL: https://github.com/gabmardakhaev LinkedIn URL: https://www.linkedin.com/in/gabriel-mardakhaev Special Thanks to Contributors We want to express our deepest gratitude to the following contributors, whose ongoing support and dedication led to the success of this project. Lee Stott, Principal Cloud Advocate Microsoft He Ye, Academic Supervisor, UCL Jens Krinke, Senior Lecturer and Academic Supervisor, UCL
How do you structure a Power BI learning path for beginners?
I would structure a beginner Power BI learning path around the complete reporting workflow, starting with the data and gradually moving toward analysis and sharing. 1. Start with Power BI fundamentals Understand the difference between Power BI Desktop and the Power BI service, along with reports, visuals, semantic models, filters, and dashboards. 2. Learn how to connect to data Start with simple sources such as Excel and CSV files. Then explore other commonly used data sources as your confidence improves. 3. Build Power Query skills Learn how to clean and transform data before creating reports. Important topics include changing data types, removing duplicates, handling missing values, splitting columns, merging queries, and appending data. 4. Understand data modeling Learn how tables relate to each other and why a well-designed model matters. Focus on relationships, primary and foreign keys, fact and dimension tables, and basic model design. 5. Create basic reports Practice using charts, tables, cards, slicers, filters, and drill-through. Rather than concentrating only on visual design, try to build reports that answer specific business questions. 6. Introduce DAX gradually Once the data model is understood, start with simple measures using functions such as SUM, COUNT, DISTINCTCOUNT, and CALCULATE. More advanced DAX can be introduced after the fundamentals are comfortable. 7. Publish and manage reports Finally, learn how to publish reports to the Power BI service, work with workspaces, configure data refresh, and understand appropriate sharing and access options. A useful beginner project is a simple sales dashboard. Take an Excel dataset, clean it with Power Query, create a basic data model, write a few measures, build the report, and publish it. This gives beginners practical experience with the entire Power BI workflow. The main idea is to learn in the below order data preparation data modeling visualization DAX publishing This provides a stronger foundation than trying to learn advanced Power BI features immediately. From my perspective as a career and learning professional associated with Edoxi Kuwait, I find that beginners usually make better progress when they understand how each Power BI skill connects to an actual reporting task, rather than trying to learn all the available features at once.Hands on webinar: Build Student Skills with Learning Accelerators in Microsoft Teams
Join us on Wednesday, September 16th @ 8am Pacific Time and discover how Learning Accelerators in Microsoft Teams for Education can help students build foundational and future ready skills while giving educators actionable insights to personalize instruction. In this webinar, we’ll explore the latest capabilities across the Learning Accelerators suite, including: 📖 Reading Progress - Help students build reading fluency with personalized practice and AI-powered feedback 🎤 Speaker Progress – Develop confident presentation and communication skills with real-time coaching ➗ Math Progress – Create, assign, and review math practice while gaining deeper insight into student understanding 🔎 Search Progress – Teach students how to search effectively, evaluate sources, and build information literacy skills 📊 Education Insights – Turn learning activity into actionable data to identify trends and better support individual students We’ll walk through practical classroom scenarios, demonstrate key features, and share tips for getting the most from Learning Accelerators in Microsoft Teams. Join us to see how Learning Accelerators can help educators save time, personalize learning, and give every student more opportunities to practice, improve, and succeed. Wednesday, September 16th @ 8am Pacific time: Register here: https://df.events.teams.microsoft.com/event/df.2cc31453-8761-4442-9b9e-eceb4a80859f@72f988bf-86f1-41af-91ab-2d7cd011db47?source=copyLinkOneEventsShareDialog Mike Tholfsen Group Product Manager Microsoft EducationFour opportunities for IT admins to get ready with Copilot Chat for education
Microsoft Education is offering four live webinar opportunities for IT administrators who want to confidently deploy and manage Microsoft 365 Copilot Chat. Each session covers the same practical guidance, so you can select the date that best fits your schedule. What you’ll learn We’ll walk through Copilot Chat configuration from the tenant level to the end-user experience, including options for students ages 13+, CSV, SDS, and PowerShell uploads, agents and extensibility, and current licensing and security controls. You’ll leave with actionable guidance to help deploy, manage, and optimize Copilot Chat safely across your organization. We’ll also share a brief look at some Microsoft Education experiences including Teach in Copilot, the Study and Learn agent, Copilot Notebooks, and Learning Activities. Choose a session and register September 16th, Wed @ 8am Pacific time - Register for the September 16 session September 30th, Wed @ 8am Pacific time - Register for the September 30 session October 14th, Wed @ 8am Pacific time - Register for the October 14 session October 28 th , Wed @ 8am Pacific time - Register for the October 28 session Reserve your spot today. Pick the session that works best for you and join us to build a secure, manageable path to Copilot Chat adoption in your education environment. Mike Tholfsen Group Product Manager Microsoft EducationWhat's New in Microsoft EDU - Back to School August 2026
Welcome to our monthly update for Microsoft Education and thank you so much for being part of our growing community! Topics Teach in Microsoft Copilot updates Learning Zone updates Teams for Education updates Study and Learn agent updates Copilot Notebooks and OneNote updates Learning management system integration updates We’ve loved seeing the early impact at schools and institutions such as Brisbane Catholic Education and Miami Dade College and the exploration of new capabilities like agents. Get started with Copilot Chat, our no-cost AI-powered chat with data protection, and learn more about Microsoft 365 Copilot. 1. Teach in Microsoft Copilot updates Teach remembers your classes and settings Educators no longer need to re-enter their subject and grade level every time they use an AI creation tool. Remember Settings automatically saves previously-used Subjects and Grades and carries them across Teach, Teams, LTI, and OneNote Class Notebook. Set your course information once and reuse it, or switch between multiple courses in one click. This means a teacher with "Biology, Grade 9" and "Chemistry, Grade 11" can move between AI tools - lesson plans, quizzes, flashcards, rubrics - without ever filling in the same fields twice. Course details appear as a chip in the upper right of creation form, replacing blank fields with familiar context from day one. When: Available now Teach in your language and region AI creation tools across Teach, Teams, and Classwork now automatically detect language from the educator's content and browser settings - no manual language picker required. Whether you're working in Spanish, French, Japanese, or any supported language, generated content matches your actual teaching language without extra steps. When: Available now Unit Plans in Teach Unit Plans, the most requested tool in Teach, arrives soon! It brings standards, learning goals, pacing, lessons, and assessments together in one coherent plan for the entire unit. Set the unit length and weekly teaching schedule and ground it on your standards and materials. Teach builds a structured week-by-week sequence, maps standards to individual lessons, and places assessments at the right points across the unit. From there, you can refine the pacing, reorder lessons, and create connected lesson plans and assessments while keeping the whole unit in view. When: Coming September 2. Microsoft Learning Zone updates As AI becomes part of everyday learning, classroom experiences are becoming more active and interactive. Teachers are no longer limited to presenting content while students passively consume it. Instead, AI is helping teachers create engaging lessons faster, while giving students more opportunities to participate, practice, and receive feedback in real time. Learning Zone was built to support this shift, connecting AI-powered interactive lesson creation, classroom delivery, and learning insights in one experience. Create lessons on any Windows 11 device This school year, educators on any Windows 11 PC can create Learning Zone's interactive lessons through August 2027. Teachers can create lessons of up to 10 slides in minutes from an idea, standard, or trusted content source, making it easier to bring relevant and customized learning experiences into the classroom while saving valuable preparation time. When: Available Now Make classroom sessions more engaging and active Learning Zone is also expanding to support engaging live classroom experiences through educator-led interactive lessons. Students can join from their own devices and participate together in a live lesson experience, while teachers guide instruction, control pacing, and receive real-time insights into student understanding. When: Available Now Create lessons in more languages This September, Learning Zone is also expanding lesson creation support to French, Italian, Portuguese, and Japanese, alongside English and Spanish, helping more educators create lessons in their preferred language. When: September Interactive lessons in Copilot Teach and in Teams assignments To fit naturally into existing teaching workflows, Learning Zone integrates with Teach in the Microsoft 365 Copilot app, Teams Assignments, and LMS platforms through Microsoft LTI. Lessons can be created and assigned from the tools educators already use, while students complete work in familiar environments and grades sync automatically. When: Available Now And we're not stopping there. Based on ongoing feedback, Learning Zone continues to evolve with new capabilities that make lesson creation faster, teaching more effective, and learning more accessible and engaging. Recent additions include standards lookup, enhanced image support through Bing integration, new interactive activity types, Immersive Reader integration, and tablet support for students, with more improvements on the way. Together, these updates make it easier for educators to save time, create engaging learning experiences, and make purposeful use of the devices already in their classrooms. 3. Teams for Education updates Learning Activities in Assignments and Classwork Learning Activities - flashcards, fill in the blanks, matching, and quizzes - can now be added directly within Assignments and Classwork in Teams. Educators can attach an existing Learning Activity or create a new one without leaving the assignment workflow. Student progress is captured as part of the assignment experience, keeping interactive practice and review in one familiar place. Students can also create their own Learning Activities from educator-shared resources within an assignment. Share a document or PDF, and students can generate flashcards or study activities from it, supporting self-directed learning without extra setup from the teacher. When: Available Now Group assignments updates Creating differentiated groups for assignments just got easier. Educators can now reuse student groups from previous assignments - select a past assignment, pick the groups you want, and apply them to a new assignment in seconds. No more recreating the same reading groups, project teams, or skill-level clusters from scratch each time. After publishing an assignment, educators can move students between groups without recreating the assignment. Need to shift a student from one reading level to another mid-unit? A single "move member" action handles it cleanly while preserving existing student work. When: Available Now Control when students can undo turn-in Educators can now choose whether students can undo a submission after turning in an assignment. When creating or editing an assignment, set Students can undo turn-in to No to make each submission final until you return the work for revision. The setting can be changed for each assignment, and educators can choose their preferred default for new assignments in Assignments Settings. When: Available Now 4. Study and Learn agent updates Study and Learn Agent is available at aka.ms/studyandlearn in your Microsoft Education tenant. An AI coach based on learning science, it tutors learners by preserving productive struggles essential for learning instead of just giving answers or doing the work for them. Copilot chat needs to be enabled for K12 students 13-17 years to get access to the agent (see tutorial video for exact enablement steps). It is available and pinned for all adult learners by default. Copilot chat now opens with Study and Learn pre-selected For K-12 students who already have Copilot Chat enabled, Study and Learn Agent is now pre-selected when they start using Copilot Chat. Students can dismiss the agent. IT admins control this setting in the Education settings panel in the Microsoft 365 admin center. It is ON by default for K-12 and OFF by default for higher education, where admins can enable it. Study and Learn includes images to “show” instead of “tell” concepts Study and Learn conversations can now include a carousel of relevant, web-safe images sourced through search. Images support the next step in a student’s understanding, helping them work toward their own answer while preserving productive effort. Further improvements to images are planned. Available in 40 languages The agent is now available in 40 languages beyond English (US). Set your browser or operating system to a supported locale and visit https://aka.ms/studyandlearn. Learning activities will appear in your chosen app locale, and you can switch languages during a conversation for a multilingual learning experience. 5. Copilot Notebooks and OneNote updates We’re expanding what educators can create and do across OneNote, with new Copilot experiences coming along for the ride: Share links from Learning Activities now work across OneNote on the web, desktop, iPad, and iOS. When pasted into a OneNote page, these links automatically unfurl into interactive activities directly on the page. Coming soon to preview for Technology Adoption Program (TAP) customers: Copilot Teaching Tools directly in the OneNote Class Notebook toolbar. Want to try upcoming features early? Ask your IT admin to complete the form at aka.ms/joinEDUTAP to join TAP. Copilot Notebooks and Study guide are available to all educators and students Copilot Notebooks are available to all Microsoft 365 Education licenses (A1, A3, A5) without requiring an additional Copilot Premium license. Copilot Notebooks are AI-powered workspaces to gather all your materials on a single topic, chat with Copilot and create Study guide with interactive activities like flashcards, quizzes, matching and fill-in-the-blanks, Mind map, Word, Excel, and PowerPoint. 6. Learning management system integration updates Support for new learning management systems Welcome Skooler, Infinite Campus, and Blackbaud into our officially supported LMS platforms alongside Canvas, Blackboard, D2L/Brightspace and Moodle. The Microsoft 365 LTI app is LTI 1.3 Advantage Complete certified so able to support any platform that is conformant to the LTI 1.3 Advantage standard, but we partner closely with these LMS platforms to make sure our customers have a great experience. Copilot teaching tools are generally available Copilot teaching tools are a set of tools available in familiar LMS flows for creating and updating content. Educators can easily access AI-powered teaching tools to quickly make modifications to language, reading level, length, difficulty, alignment to relevant standards. They can also create Microsoft Learning Activities like flashcards, fill in the blanks, and matching games that can be shared with a class as study aids. Meetings keep in sync and recordings view Keeping your current roster in sync with meeting invites is easy – Enabling the keep in sync option in meetings ensures that your meeting invites to the are always in sync with the current LMS roster and students can co-teachers have invites and can join your meetings. We have also added a new (default) option for meeting recordings that allows teachers to explicitly publish recordings for the class, ensuring that all students and co-teachers have access to view or edit the recording and also giving a chance for educators to add captions or edit recordings before publishing for the class. Add co-teachers as meeting co-organizers The new setting to add co-teachers in a course as co-organizers to a meeting allows you to easily give your co-teachers in a course the ability to help you manage meetings and moderate online sessions. OneDrive 2tb file upload limit File upload limit for sharing files with a course is now 2tb, ensuring that large files like meeting recordings can be shared with a course without issues. ____________________________________ And finally, just to recap all the news we have for you this month, here’s a quick review of all the features that are generally available or are rolling out now: Teach module in Microsoft Copilot updates Settings to remember your classes Language and regional settings auto-detect and improvements Unit Plans Microsoft Learning Zone Available as a trial for Windows 11 Integration in to Teach, Teams and LMS Teach in Class mode Standards support New languages Improved images Teams for EDU updates Learning Activities in Assignments Reuse Groups Undo Turn-in Study and Learn agent updates IT Admin controls to enable "pre-selected" as the default 40+ languages supported Image improvements Copilot Notebooks available to all M365 education teachers and students LMS integration and Microsoft 365 LTI Support for new LMSes - Skooler, Infinite Campus and Blackbaud Copilot teaching tools generally available Meetings keep in sync and recording views Add co-teachers as meeting co-organizers OneDrive 2tb file upload limit Mike Tholfsen Group Product Manager Microsoft Education
