AI Based Safe Surfing System

Abstract

As internet usage continues to grow rapidly among children and teenagers, safeguarding their online experience has become increasingly important. The open nature of search engines exposes young users to inappropriate and potentially harmful content that may include explicit language, violent media, or misleading information. This project titled "Safe Search for Kids" addresses this problem by creating a child-friendly web search system that filters and moderates content based on predefined rules and advanced AI moderation techniques.
Our system is built using a layered filtering architecture that starts with a static blocklist check, progresses to pattern recognition for disguised terms, and concludes with a context-aware evaluation powered by the OpenAI API for ambiguous or unknown queries. This multi-layered approach significantly enhances the accuracy and reliability of the moderation system.
By classifying queries into "Safe," "Blocked," and "Under Construction," the system provides a user-friendly experience while maintaining safety. The project combines web development, content moderation techniques, and machine learning APIs to present a practical solution to an important real-world issue.
With potential for integration in homes, schools, and libraries, the system can serve as a first step towards building a safer digital environment for the next generation of internet users.