Step-by-Step: Setting Up GitHub Student and GitHub Copilot as an Authenticated Student Developer
To become an authenticated GitHub Student Developer, follow these steps: create a GitHub account, verify student status through a school email or contact GitHub support, sign up for the student developer pack, connect to Copilot and activate the GitHub Student Developer Pack benefits. The GitHub Student Developer Pack offers 100s of free software offers and other benefits such as Azure credit, Codespaces, a student gallery, campus experts program, and a learning lab. Copilot provides autocomplete-style suggestions from AI as you code. Visual Studio Marketplace also offers GitHub Copilot Labs, a companion extension with experimental features, and GitHub Copilot for autocomplete-style suggestions. Setting up your GitHub Student and GitHub Copilot as an authenticated Github Student DeveloperRedeeming Azure for Student from your GitHub Student Pack when you do not have an Academic Email
GitHub Student Developer Pack Learn to ship software like a pro. There's no substitute for hands-on experience. But for most students, real world tools can be cost-prohibitive. That's why we created the GitHub Student Developer Pack with some of our partners and friends. Sign up for Student Developer PackAI Career Navigator — Empowering Job Seekers with Azure OpenAI
AI Career Navigator is more than just a project — it’s a mission to make career growth accessible, intelligent, and human. Powered by Azure OpenAI, it transforms uncertainty into direction and effort into achievement. Author: Aryan Jaiswal — Gold Microsoft Learn Student Ambassador Reviewer: Julia Muiruri (Microsoft)
Microsoft’s A-Grade Azure AI Stack: From Dissertation Prototype to Smart Campus Pilot
This post isn't just about the Student Support Agent (SSA) I built, which earned me a Distinction. It's about how Microsoft's tools made it possible to go from a rough concept to a robust pilot, proving their developer stack is one of the most convenient and powerful options for building intelligent, ethical, and scalable educational systems. The Vision: Cutting Through Campus Complexity University life is full of fragmented systems. Students constantly juggle multiple logins, websites, and interfaces just to check a timetable, book a room, or find a policy. My goal was simple: reduce that cognitive load by creating a unified assistant that could manage all these tasks through a single, intelligent conversation. The Stack That Made It Possible The core of the system relied on a few key, interconnected technologies: Technology Core Function Impact Azure AI Search Hybrid Data Retrieval Anchored responses in official documents. Azure OpenAI Natural Language Generation Created human-like, accurate answers. Semantic Kernel (SK) Multi-Agent Orchestration Managed complex workflows and memory. Azure Speech SDK Multimodal Interface Enabled accessible voice input and output. The foundation was built using Streamlit and FastAPI for rapid prototyping. Building a system that's context-aware, accessible, and extensible is a huge challenge, but it's exactly where the Microsoft AI stack shined. From Simple Chatbot to Multi-Agent Powerhouse Early campus chatbots are often single-agent models, great for basic FAQs, but they quickly fail when tasks span multiple services. I used Semantic Kernel (SK) Microsoft's powerful, open-source framework to build a modular, hub-and-spoke multi-agent system. A central orchestrator routes a request (like "book a study room") to a specialist agent (the Booking Agent), which knows exactly how to handle that task. This modularity was a game-changer: I could add new features (like an Events Agent) without breaking the core system, ensuring the architecture stayed clean and ready for expansion. Agentic Retrieval-Augmented Generation (Agentic RAG): Trust and Transparency To ensure the assistant was trustworthy, I used Agentic RAG to ground responses in real campus (Imperial College London) documentation. This included everything from admission fee payments to campus shuttle time. Azure AI Search indexed all handbooks and policies, allowing the assistant to pull relevant chunks of data and then cite the sources directly in its response. Result: The system avoids common hallucinations by refusing to answer when confidence is low. Students can verify every piece of advice, dramatically improving trust and transparency. Results: A Foundation for Scalable Support A pilot study with 15 students was highly successful: 100% positive feedback on the ease of use and perceived benefit. 93% satisfaction with the voice features. High trust was established due to transparent citations. The SSA proved it could save students time by centralising tasks like booking rooms, checking policies and offering study tips! Final Thoughts Microsoft’s AI ecosystem didn’t just support my dissertation; it shaped it. The tools were reliable, well-documented, and flexible enough to handle real-world complexity. More importantly, they allowed me to focus on student experience, ethics, and pedagogy, rather than wrestling with infrastructure. If you’re a student, educator, or developer looking to build intelligent systems that are transparent, inclusive, and scalable, Microsoft’s AI stack is a great place to start! 🙋🏽♀️ About Me I’m Tyana Tshiota, a postgraduate student in Applied Computational Science and Engineering at Imperial College London. Leveraging Microsoft’s AI stack and the extensive documentation on Microsoft Learn played a key role in achieving a Distinction in my dissertation. Moving forward, I’m excited to deepen my expertise by pursuing Azure certifications. I’d like to extend my sincere gratitude to my supervisor, Lee_Stott , for his invaluable mentorship and support throughout this project. If you haven’t already, check out his insightful posts on the Educator Developer Blog, or try building your own agent with the AI Agents for Beginners curriculum developed by Lee and his team! You can reach out via my LinkedIn if you’re interested in smart campus systems, AI in education, collaborative development, or would like to discuss opportunities.Como começar e crescer no mercado de tecnologia
A #JornadaTech é uma maratona de mentorias online do Microsoft Reactor São Paulo, sobre carreira na tecnologia, em que você poderá aprender mais sobre as áreas de Cloud, Segurança, Programação e Dados. Neste artigo, você encontrará algumas dicas e recursos para começar e crescer na carreira de tecnologia.
Streamlining Campus Life: A Multi-Agent System for Campus Event Management
Introduction Managing campus events has long been a complex, manual process fraught with challenges. Traditional event management systems offer limited automation, placing a considerable workload on staff for tasks ranging from resource allocation to participant communication. This procedural friction presents a clear opportunity to build a more intelligent solution, leveraging the emerging paradigm of AI agents. To solve these challenges, I developed and evaluated a multi-agent system designed to automate the campus event workflow and improve productivity. In this blog, I’ll share the journey of building this system, detailing its architecture and how I leveraged the Semantic Kernel and Azure Services to create a team of specialized agents. Background My name is Junjie Wan, and I’m a MSc student in Applied Computational Science and Engineering at Imperial College London. This research project, in collaboration with Microsoft, explores the development of a multi-agent solution for managing a university campus. The system's focus is on automating the event management workflow using Microsoft Azure AI Agent services. I would like to thank my supervisor, Lee Stott, for his guidance and mentorship during this project. Methodology: Building the Agentic System. The Model Context Protocol (MCP) and Backend Integration For agents to perform their duties effectively, they need access to a powerful set of tools. The system's backend is a high-performance API built with FastAPI, with Azure Cosmos DB serving as the scalable data store. To make these API functions usable by the agents, they are wrapped as tools using Semantic Kernel’s kernel_function decorator. These tools contain the necessary functions to utilize both the internal API and various Azure Services. The setup for making these tools accessible is straightforward: we first instantiate a central Kernel object, add the defined tools as plugins, and then convert this populated Kernel into a runnable MCP server. This approach creates an extensible system where new tools can be added as services without requiring changes to the agents themselves. Frontend Implemenation with Streamlit To build a frontend powered by the AI features and based on Python, I choose to use the Streamlit for rapid prototyping. The frontend implements role-based access control, with different interfaces for admin, staff, and students. The system inlcudes a dashbarad, form-based pages, and a conversational chat interface as the primary entry point for the multi-agent system. To enhance user experience, it supports multi-modal input through voice integration, which uses OpenAI whisper for accurate speech-to-text transcription and the OpenAI tts model in Azure AI Foundry for voice playback. Individual Agent Design The system distributes responsibilities across a team of specialized agents, each targeting a specific operational aspect of event management. Each agent is initialized as a ChatCompletionAgent with OpenAI’s Model Router and MCP plugins. Here are some of the agents implemented to improve the event management process. To address the operational challenge of manually reconciling room availability and event requirements, the system utilizes a Planning Agent and a Schedule Agent. The Planning Agent serves as the central coordinator, gathering event specifications from the user. It can even leverage the Azure Maps Weather service to provide organizers with weather forecasts that may influence venue selection. It then delegates to the Schedule Agent, which is responsible for generating conflict-free timetable entries by querying our FastAPI backend for real-time availability data stored in the database. This workflow directly replaces the error-prone manual process and prevents scheduling conflicts. For financial planning, the Budget Agent functions as the system's dedicated financial analyst, designed to solve the problem of inaccurate cost estimation. When tasked with a budget, it first retrieves the event context from Cosmos DB. To ground its responses in verifiable data, the agent utilizes a Retrieval-Augmented Generation (RAG) pipeline built on Azure AI Search. This allows it to search internal documents, such as catering menus, for pricing information. If items are not found internally, the agent uses the Grounding with Bing Search tool to gather real-time market data, ensuring estimations are both accurate and current. To automate the manual, time-consuming process of participant communication, the Communication Agent handles all interactions. It drafts personalized emails populated with event details retrieved from the database. The agent is equipped with a tool that directly interfaces with Azure Communication Service to send emails programmatically. This automates the communication workflow, from sending initial invitations with Microsoft Forms links for registration to distributing post-event feedback surveys, saving significant administrative effort. Multi-Agent Collaboration For collaboration between agents, I chose the AgentGroupChat pattern within Semantic Kernel. While orchestration patterns like sequential or handoff are suitable for linear tasks and dynamic delegation between agents, the multi-domain nature of event management required a more flexible approach. The group chat pattern allows for both structured sequential handoffs and dynamic contributions from other agents as needed. Group Chat Design The orchestration logic is governed by two dynamic, LLM-driven functions: Selection Function: This acts as a dynamic router, analyzing the conversation's context to determine the most appropriate agent to speak in the next round. It performs intent recognition for initial queries and routes subsequent turns based on the ongoing workflow. Termination Function: This function prevents infinite loops and ensures the system remains responsive. It evaluates each agent's turn to decide whether the conversation should conclude or if a clear handoff warrants its continuation, maintaining coherent system behavior. Evaluation Framework and Performance To evaluate whether the system could reliably execute domain-specific workflows, I used the LLM-as-a-Judge framework through the Azure AI Evaluation SDK, which provides a scalable and consistent assessment of agent performance. Group Chat Performance Radar Chart The evaluation focused on three main categories of metrics to get a holistic view of the system: Functional Correctness: I used metrics such as IntentResolution, TaskAdherence, and ToolCallAccuracy to assess whether the agents correctly understood user requests, followed instructions, and called the appropriate tools with the correct parameters. Response Quality: Metrics like Fluency, Coherence, Relevance, and Response Completeness were used to evaluate the linguistic quality of the agents' responses. Operational Metrics: To assess the practical viability of the system, I also measured key operational metrics, including latency and token usage for each task. The results confirmed the system's strong performance, consistently exceeding the pass threshold of 3.0. This demonstrates that the agentic architecture can successfully decompose and execute event management tasks with high precision. In contrast, linguistic metrics were lower, highlighting a potential trade-off where our multi-agent system focuses on functionality prioritized over conversational flow. The operational metrics also provided valuable insights into system behavior: Response Time by Tag Token vs Tool Call Latency: The data showed that simpler queries, such as reading information, were consistently fast. However, complex, multi-step tasks exhibited significantly longer and more variable response times. This pattern reflected the expected accumulation of latency across multiple agent handoffs and tool calls within the agentic workflow. Token: Analysis revealed a strong positive correlation between the number of tool calls and total token consumption, indicating that workflow complexity directly impacted computational cost. The baseline token usage for simple queries is high largely due to the context of tool definitions injected by the MCP server. Agents relying on RAG pipelines, like the Budget Agent, notably consumed more tokens due to the inclusion of retrieved context chunks in their prompts. Limitation and Future Work Despite the good performance, the system has several limitations: The system relies on carefully engineered prompts, making it less flexible when facing unexpected queries. Multi-turn coordination between agents and the use of MCP servers results in high token consumption, raising concerns about efficiency and scalability in production deployments. The system was tested with synthetic data and a relatively small set of test queries, which may not reflect the complexity of real-world scenarios. Future work will focus on: Enhancing error handling and recovery mechanisms within the group chat orchestration Improving conversational quality while reducing token consumption Deploying the agent system on a server for broader access and collecting user feedback Testing the system with real-world data and conducting formal user studies Conclusion This project demonstrates that a multi-agent system, built on the integrated power of Microsoft Azure services, can offer an efficient solution for campus event management. By dividing the labor among specialized agents and enabling them with a powerful toolkit, we can automate complex workflows and reduce administrative burden. This work serves as a proof-of-concept that shows how agentic approaches can deliver more intelligent and streamlined solutions that improve the quality of events and the student experience. Thank you for reading! If you have any questions or would like to discuss this work further, please feel free to contact me via email or on LinkedIn.Model Mondays S2E13: Open Source Models (Hugging Face)
1. Weekly Highlights 1. Weekly Highlights Here are the key updates we covered in the Season 2 finale: O1 Mini Reinforcement Fine-Tuning (GA): Fine-tune models with as few as ~100 samples using built-in Python code graders. Azure Live Interpreter API (Preview): Real-time speech-to-speech translation supporting 76 input languages and 143 locales with near human-level latency. Agent Factory – Part 5: Connecting agents using open standards like MCP (Model Context Protocol) and A2A (Agent-to-Agent protocol). Ask Ralph by Ralph Lauren: A retail example of agentic AI for conversational styling assistance, built on Azure OpenAI and Foundry’s agentic toolset. VS Code August Release: Brings auto-model selection, stronger safety guards for sensitive edits, and improved agent workflows through new agents.md support. 2. Spotlight – Open Source Models in Azure AI Foundry Guest: Jeff Boudier, VP of Product at Hugging Face Jeff showcased the deep integration between the Hugging Face community and Azure AI Foundry, where developers can access over 10 000 open-source models across multiple modalities—LLMs, speech recognition, computer vision, and even specialized domains like protein modeling and robotics. Demo Highlights Discover models through Azure AI Foundry’s task-based catalog filters. Deploy directly from Hugging Face Hub to Azure with one-click deployment. Explore Use Cases such as multilingual speech recognition and vision-language-action models for robotics. Jeff also highlighted notable models, including: SmoLM3 – a 3 B-parameter model with hybrid reasoning capabilities Qwen 3 Coder – a mixture-of-experts model optimized for coding tasks Parakeet ASR – multilingual speech recognition Microsoft Research protein-modeling collection MAGMA – a vision-language-action model for robotics Integration extends beyond deployment to programmatic access through the Azure CLI and Python SDKs, plus local development via new VS Code extensions. 3. Customer Story – DraftWise (BUILD 2025 Segment) The finale featured a customer spotlight on DraftWise, where CEO James Ding shared how the company accelerates contract drafting with Azure AI Foundry. Problem Legal contract drafting is time-consuming and error-prone. Solution DraftWise uses Azure AI Foundry to fine-tune Hugging Face language models on legal data, generating contract drafts and redline suggestions. Impact Faster drafting cycles and higher consistency Easy model management and deployment with Foundry’s secure workflows Transparent evaluation for legal compliance 4. Community Story – Hugging Face & Microsoft The episode also celebrated the ongoing collaboration between Hugging Face and Microsoft and the impact of open-source AI on the global developer ecosystem. Community Benefits Access to State-of-the-Art Models without licensing barriers Transparent Performance through public leaderboards and benchmarks Rapid Innovation as improvements and bug fixes spread quickly Education & Empowerment via tutorials, docs, and active forums Responsible AI Practices encouraged through community oversight 5. Key Takeaways Open Source AI Is Here to Stay Azure AI Foundry and Hugging Face make deploying, fine-tuning, and benchmarking open models easier than ever. Community Drives Innovation: Collaboration accelerates progress, improves transparency, and makes AI accessible to everyone. Responsible AI and Transparency: Open-source models come with clear documentation, licensing, and community-driven best practices. Easy Deployment & Customization: Azure AI Foundry lets you deploy, automate, and customize open models from a single, unified platform. Learn, Build, Share: The open-model ecosystem is a great place for students, developers, and researchers to learn, build, and share their work. Sharda's Tips: How I Wrote This Blog For this final recap, I focused on capturing the energy of the open source AI movement and the practical impact of Hugging Face and Azure AI Foundry collaboration. I watched the livestream, took notes on the demos and interviews, and linked directly to official resources for models, docs, and community sites. Here’s my Copilot prompt for this episode: "Generate a technical blog post for Model Mondays S2E13 based on the transcript and episode details. Focus on open source models, Hugging Face, Azure AI Foundry, and community workflows. Include practical links and actionable insights for developers and students! Learn & Connect Explore Open Models in Azure AI Foundry Hugging Face Leaderboard Responsible AI in Azure Machine Learning Llama-3 by Meta Hugging Face Community Azure AI Documentation About Model Mondays Model Mondays is your weekly Azure AI learning series: 5-Minute Highlights: Latest AI news and product updates 15-Minute Spotlight: Demos and deep dives with product teams 30-Minute AMA Fridays: Ask anything in Discord or the forum Start building: Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! The Azure AI Developer Community is here for real-time chats, events, and support: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.Pantry Log–Microsoft Cognitive, IOT and Mobile App for Managing your Fridge Food Stock
First published on MSDN on Mar 06, 2018 We are Ami Zou (CS & Math), Silvia Sapora(CS), and Elena Liu (Engineering), three undergraduate students from UCL, Imperial College London, and Cambridge University respectively.Model Mondays S2E8: On-Device & Local AI
Model Mondays S2E8: On-Device & Local AI Welcome to Episode 8! This week, we explored how AI is moving from the cloud to your own device, making it faster, more private, and more accessible. We also saw a real-world customer story from Xander Glasses, showing how AI can help people with hearing loss. RFD Observability tools in Azure AI Foundry: Real-time model telemetry, auto evals, quick evals, Python grader. GitHub Copilot Pro with Spark: AI pair programmer for code explanation and workflow suggestions. Synthetic Data for Vision Models: Training accurate models with procedurally generated data. Agent-Friendly Websites: Making sites accessible to AI agents via APIs, semantic markup, and OpenAPI specs. MCP (Model Context Protocol): Standardizing agent memory and context for scalable AI.
What is GitHub Codespaces and how can Students access it for free?
GitHub Codespaces is a new service that is free for anyone to develop with powerful environments using Visual Studio Code. In this post, we'll cover how you can make use of this new technology and take advantage of its most powerful features.
