ACADEMIQ - AI Architecture for Predictive EdTech
Project Goal
ACADEMIQ is a conceptual research project that investigates how AI can revolutionize student support in higher education. The focus is on predictive analytics—identifying students who may struggle before they fall behind—and personalizing educational content delivery.
Deep Learning Core
The core of ACADEMIQ is a collection of deep learning models developed in Python using:
- TensorFlow: Used for high-level abstraction and rapid experimentation with various model types (e.g., recurrent neural networks for sequence data or dense networks for feature prediction).
- PyTorch: Employed for more intricate, low-level control over custom neural network architectures, primarily for fine-tuning performance on complex behavioral data.
The system ingests data such as class participation, assessment scores, and engagement logs to predict future performance metrics with high accuracy.
Scalability and Integration
While the initial prototypes used simple HTML/CSS mockups for visualization, the architectural planning was paramount. We focused on designing a scalable pipeline where data could be continuously fed to the models, and predictions could be served back to the university’s web interface with minimal latency. This project heavily emphasized the practical implementation of academic AI research into deployable, real-world systems.