Skillatics: An AI-Driven Adaptive Learning and Assessment Platform for Personalized Skill Mastery and Placement Readiness

In the current competitive placement landscape, tra- ditional learning platforms fail to provide personalized, adaptive, and reliable assessment mechanisms, leading to inefficient prepa- ration and poor skill evaluation. This paper presents Skillatics, an AI-driven adaptive learning and assessment platform designed to enhance student placement readiness through a unified and data-driven approach. The system integrates an adaptive test engine that dynamically adjusts question difficulty based on real- time student performance, ensuring personalized learning paths. It also incorporates a secure, sandboxed coding environment using containerized execution for technical assessments, along with a lightweight proctoring system to maintain academic integrity. Furthermore, an AI-powered analytics module identifies topic-wise skill gaps and provides actionable feedback, enabling targeted improvement. To enhance engagement, the platform includes gamification features such as experience points, leader- boards, and interactive learning modes. The proposed system is implemented as a scalable web-based application using modern technologies including React, Flask, MongoDB, and external APIs such as Piston and Google Gemini. The results demonstrate that integrating adaptive learning, secure assessment, and analytics into a single platform significantly improves learning efficiency, engagement, and overall placement preparedness.