Project

IScheduler

Experimental web-based project developed in Angular, conceived as an early attempt to integrate artificial intelligence techniques into the management and simulation of job interviews.

The project was designed as a research-oriented prototype, aimed at exploring the feasibility of using interactive and visual analysis to support candidates in improving their interview performance.

The core idea behind iScheduler was to simulate a realistic job interview experience in a controlled digital environment. Through a structured interview flow, the system guides the user through simulated interview sessions while collecting interaction data. The long-term vision of the project was to incorporate computer vision and webcam-based analysis to observe non-verbal cues, posture, facial expressions, and behavioral patterns, providing feedback on communication effectiveness and professional demeanor.

The application architecture is built around a role-based system, with administrative and user dashboards. The administrative side was intended to manage interview sessions, users, and scheduled events, while the user-facing side focuses on participating in simulated interviews and reviewing assigned sessions. This structure reflects the project’s ambition to model real-world interview workflows while maintaining flexibility for experimentation.

A key aspect of the design is its focus on training rather than evaluation. Instead of passively assessing candidates, iScheduler was conceived as an interactive coaching tool, offering feedback, improvement suggestions, and targeted exercises to help users refine both verbal and non-verbal communication skills. The system was designed to progressively adapt interview scenarios to the user’s performance, encouraging self-awareness and continuous improvement.

Although the project was not fully completed, it successfully implements the foundational components needed to validate the concept. Core functionalities such as authentication, role-based navigation, event scheduling, and interview session management are in place, serving as a proof of concept for a more advanced AI-driven training platform.