The rapid growth of digital education platforms has significantly transformed competitive examination preparation; however, a substantial proportion of learners in developing and semi-urban regions continue to face challenges associated with high subscription costs, device incompatibility, excessive bandwidth consumption, and limited personalization support. Existing large-scale e-learning ecosystems often depend on backend-intensive infrastructures and resource-heavy frameworks, resulting in increased latency, reduced accessibility on low-specification devices, and inconsistent performance under constrained network conditions. To address these limitations, this research presents \textit{SkillHub}, a lightweight AI-ready web platform specifically designed for personalized competitive exam preparation with emphasis on accessibility, responsiveness, and low computational overhead. The proposed framework adopts a purely client-side modular architecture implemented using HTML5, CSS3, JavaScript, and jQuery, thereby eliminating continuous server dependency during standard learning operations. The system integrates a responsive quiz engine, structured project repository, adaptive user interface, and scalable content management workflow within a unified educational environment. Unlike conventional monolithic learning systems, the proposed architecture minimizes rendering complexity and optimizes resource utilization through lightweight DOM manipulation, lazy asset loading, asynchronous event handling, and browser-based local persistence mechanisms. The computational efficiency of the framework can be represented through reduced page interaction overhead, \[ T_{load} \propto \frac{R_s + D_m}{B_w}, \] where $T_{load}$ denotes average page load latency, $R_s$ represents resource size, $D_m$ indicates DOM manipulation complexity, and $B_w$ corresponds to available network bandwidth. By minimizing both $R_s$ and $D_m$, the platform achieves improved responsiveness even under limited connectivity conditions. The experimental evaluation was conducted across multiple browsers and device categories using BrowserStack and Chrome DevTools under simulated 3G network constraints. Performance analysis demonstrated an average load time of approximately $1.9\,s$, maintaining full cross-browser compatibility and achieving a mobile responsiveness success rate exceeding $95\%$. The quiz assessment module employed randomized question sequencing and real-time evaluation algorithms, producing a quiz completion success rate of nearly $95\%$ during peer usability testing. Furthermore, the responsive layout implementation using CSS Grid and Flexbox reduced interface breakage occurrences by nearly $80\%$ compared to conventional float-based layouts. User interaction studies additionally revealed improved navigational efficiency, where approximately $98\%$ of participants accessed core platform modules within three interaction steps. To support future intelligent learning capabilities, the proposed system is architecturally prepared for integration with TensorFlow.js recommendation models and NLP-driven conversational assistance modules. Planned extensions include adaptive study scheduling using performance-weighted recommendation functions, \[ S_r = \alpha Q_p + \beta T_w + \gamma E_f, \] where $S_r$ denotes personalized study recommendation score, $Q_p$ represents quiz performance metrics, $T_w$ indicates topic-wise weakness estimation, and $E_f$ corresponds to engagement frequency. Such integration is intended to facilitate dynamic learner profiling and personalized educational pathways without compromising lightweight operational characteristics. The presented work demonstrates that carefully optimized front-end engineering can deliver a scalable, accessible, and AI-ready educational ecosystem suitable for competitive exam aspirants operating under limited computational and network resources. The primary contribution of this research lies in establishing a high-performance lightweight learning framework that bridges the gap between accessibility, responsiveness, and future intelligent personalization within modern web-based educational systems.

Keywords: Competitive Exam Preparation, E-Learning, Lightweight Web Platform, Personalized Learning, Responsive Web Design, AI-Ready Architecture, JEE, GATE, Educational Technology