Akademia - An Intelligent Hybrid AI Learning Platform for Adaptive Education

The traditional "one-size-fits-all" pedagogical model frequently fails to address the unique cognitive speeds and learning styles of individual students, leading to disengagement and suboptimal academic outcomes. This paper introduces Akademia, an intelligent, web-based learning environment designed to facilitate truly personalized, self-paced education. Utilizing a Hybrid AI Architecture, the system integrates high-precision Optical Character Recognition (OCR) with Large Language Models (LLMs) to automate the evaluation of handwritten assessments—serving as the primary data source for student proficiency mapping. Beyond mere grading, Akademia employs a Dynamic Knowledge Tracking (DKT) model to identify granular knowledge gaps and generate adaptive learning pathways. These pathways consist of tailored resources, frequent diagnostic testing, and real-time mentor collaboration, ensuring the curriculum evolves with the student's progress. Built on Next.js 15, TypeScript, and Supabase, the architecture ensures 99% uptime and secure data isolation via Row Level Security (RLS). Experimental results indicate that students utilizing Akademia's adaptive paths exhibited a 25% faster mastery of complex concepts and a 40% increase in engagement compared to static e-learning environments. This research establishes a scalable framework for digital education where assessment and instruction are seamlessly unified into a continuous improvement loop.