🎓 EduAI Navigator

🏆 1st Place Winner – Regional Round of the AI Olympiad 2026 (Karlovy Vary Region)
🌐 👉 Try Live Demo
Team: Dva a ten Jeden
Development Time Limit: 4 hours
Development Methodology: Vibe coding with Gemini 3.5

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📌 About the Project

EduAI Navigator is an intelligent recommendation platform designed to connect student talent with the future economic and labor needs of the Karlovy Vary Region.

The application solves the challenge of inefficient secondary school selection by considering student preferences alongside regional strategic priorities, such as local industry demand and the risk of brain drain.

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📊 Dataset Specifications

ℹ️ Data Source: The dataset (dataset.xlsx) was provided as part of the official assignment for the Regional Round of the AI Olympiad 2026 and was processed/structured by our team for the purpose of this application.

The recommendation engine runs on a real dataset of 50 secondary school fields (dataset.xlsx) taught across the Karlovy Vary Region.

Dataset Attributes & Schema

• Identification: obor_id, nazev_oboru, kod_oboru
• Student Skill Profiles (1–5 scale): zajem_technika, zajem_lide, prace_rukama, prace_s_pocitacem, kreativita
• Education Balance: teorie_vs_praxe (percentage ratio of theory vs. practice)
• Regional Market Indicators: poptavka_v_kraji_1_5, uplatnitelnost_v_kraji_procent, riziko_odlivu_1_5
• Capacity & Statistics: pocet_skol_v_kraji, vyucuje_se_v, nastupni_mzda_kc, absolventi_kraj_2024, nove_prijati_kraj

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⚡ Key Features

• 6D Student Profiling: Dynamic sliders for interest in tech, working with people, manual crafts, computers, creativity, and theoretical vs. practical focus.
• Regional Priority Adjustments: Custom weighting sliders to prioritize local employer demand and mitigate brain drain risks.
• Vector Match Algorithm: Calculates 6D Euclidean distance between the user profile and all 50 dataset entries.
• Client-Side Excel Parser: Direct integration with xlsx.js to process dataset.xlsx instantly in the browser without server overhead.
• Interactive Results Dashboard: Displays TOP 3 recommended fields with detailed metrics (starting salary, employment rate, study locations) alongside a full ranking table.

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🛠️ Tech Stack

• Frontend: HTML5, CSS3 (Custom Dark Theme, Flexbox, Grid Layout), Vanilla JavaScript (ES6+).
• Libraries:
  • SheetJS (xlsx.js) – In-browser Excel parsing.
  • FontAwesome 6.5.1 – Interface iconography.
  • Google Fonts (Urbanist).
• AI Tooling: Gemini (Vibe coding, UI structure, mathematical model formulation).

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🚀 How to Run Locally

Because the application fetches dataset.xlsx via the browser's fetch() API, it must be hosted on a local web server to comply with CORS security policies.

1. Clone the repository:

   git clone https://github.com/znamirovsky/eduai-navigator.git
   cd eduai-navigator

2. Ensure File Placement: Make sure index.html and dataset.xlsx are in the same directory.

3. Start a Local Server:

   • VS Code: Install the Live Server extension, right-click index.html -> Open with Live Server.

   • Python: Run the following command in your project terminal: python -m http.server 8000

     Open http://localhost:8000 in your web browser.

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🧠 Algorithmic Model

1. Normalization: Input parameters are scaled to a unified range (1–5).
2. Personal Compatibility Score: Computes Euclidean distance in 6D vector space:
   $$Dist = \sqrt{(Tech_u - Tech_o)^2 + (Lide_u - Lide_o)^2 + (Ruka_u - Ruka_o)^2 + (PC_u - PC_o)^2 + (Krea_u - Krea_o)^2 + (Teorie_u - Teorie_o)^2}$$
3. Comprehensive Regional Score: Combines personal fit (60%) with regional market demand (20% * w_demand) and inverted brain-drain risk (20% * w_drain).

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👥 Team & Authors

Developed by team Dva a ten Jeden during the AI Olympiad 2026:

• Aaron Znamirovský – Lead Developer, Architecture & Integration
• Honza Šimurda – AI Ethics & Impact Analysis
• Tomáš Stehlík – Presentation & Pitch Strategy