𝟭. 𝗢𝘂𝗿 𝗖𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗣𝗿𝗼𝗽𝗼𝘀𝗮𝗹: 𝗧𝗵𝗲 𝗢̀𝗖𝗧𝗔𝗩𝗜𝗔 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺
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We propose the development of ÒCTAVIA, an educational and organizational support system built on a multi-agent architecture. Our technical solution transforms student needs into a concrete and operational tool. Rather than a monolithic chatbot, our contribution is a centralized platform where three distinct agents (the Planner, the Content Creator, and the Socratic agent ÒCTAVIA) interact to personalize and streamline the university experience.
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---
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𝟮. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝗴𝘂𝗮𝗿𝗱𝘀
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To address the risks of intellectual passivity and data leakage, our contribution integrates strict safeguards. We are implementing a "Safe RAG" (Retrieval-Augmented Generation) pipeline: the ÒCTAVIA agent is technically configured to absolutely prioritize the official documents provided by professors. If necessary, she can supplement this knowledge with external sources, while always maintaining the academic materials as the ultimate source of truth. Furthermore, the choice of a trusted and sovereign AI model ensures that personal data and academic intellectual property remain within a secure institutional framework.
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---
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𝟯. 𝗧𝗮𝗸𝗶𝗻𝗴 𝗮 𝗦𝘁𝗲𝗽 𝗕𝗮𝗰𝗸: 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗟𝗶𝗺𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀, 𝗮𝗻𝗱 𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀
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• 𝗘𝘅𝗽𝗲𝗰𝘁𝗲𝗱 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀: A drastic reduction in mental load and a return to active learning through Socratic maieutics. Beyond grades, our goal is to teach the integration of AI as an essential soft skill for the future professional world. ÒCTAVIA will demonstrate to reluctant students how to work smarter, while re-educating students accustomed to passive usage ("copy-pasting") to interact analytically with AI.
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• 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 (𝗧𝗵𝗲 𝗣𝗢𝗖): Our short-term goal is to deliver a functional Proof of Concept (POC) within two months. This prototype will demonstrate the viability of our ecosystem: the three agents will communicate seamlessly with each other, each perfectly executing the specific task it was designed for (planning, content creation, tutoring).
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• 𝗖𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀 𝗮𝗻𝗱 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗣𝗼𝗶𝗻𝘁𝘀: As engineers, we are fully aware of the project's challenges. The performance of our POC will depend heavily on the hardware infrastructure: there will be a notable technical difference between hosting our own models locally versus directly using the Mistral API. Furthermore, we will need to manage our dependency on the formatting quality of the professors' original documents and optimize the natural latency caused by the communication between our different agents.
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𝟰. 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻
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For this contribution to be realistic and deployable, its implementation requires specific conditions:
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1. 𝗟𝗟𝗠 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Initially, our POC will run on locally hosted Mistral models to guarantee absolute data privacy and prove our technical autonomy. However, to scale efficiently, our key implementation condition is to secure a technical partnership to access the Mistral AI API.
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2. 𝗗𝗮𝘁𝗮 𝗔𝗰𝗰𝗲𝘀𝘀: Access (even simulated) to university scheduling data feeds (e.g., Celcat format).
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3. 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲: Guidance from technical mentors to challenge our multi-agent orchestration and the security of our strict RAG pipeline.
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