John Angelo Oquiana@johnangelo_23

At the start of every semester, college students spend hours manually reading through inconsistent syllabus documents just to organize deadlines, quizzes, exams, and study materials into calendars and review apps. It is repetitive, time-consuming, and often leads to missed requirements or poor study planning. Our solution is the Syllabus-to-Study Agent: an automated AI-powered platform that transforms syllabus PDFs into structured academic tools in under a minute. Users simply upload their syllabus into a clean and distraction-free web interface. The system intelligently extracts deadlines, examination dates, quiz schedules, project milestones, and important academic content from messy or poorly formatted documents. It then automatically generates: • A ready-to-import Google Calendar CSV for semester scheduling • Anki-compatible CSV flashcard decks for quizzes, tests, and review sessions • Organized study materials that help students prepare more efficiently Behind the scenes, the application is powered by Meta-Llama-3-8B-Instruct running through a vLLM server deployed on dedicated AMD MI300X GPU hardware. Through optimized prompt engineering and structured parsing pipelines, the AI agent bypasses traditional conversational responses and produces clean, strict CSV outputs compatible with both Google Calendar and the Anki flashcard application. Built for students, by students, the platform automates both academic scheduling and study preparation, allowing users to spend less time organizing and more time learning. Technology & Category Tags Technologies • AMD MI300X • Llama 3 • vLLM • Python • Streamlit • PyPDF • CSV Processing • Anki Integration Categories • Education Tech • Productivity • AI Agent • Automation • Study Tools • Student Learning AI

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