Illuminate by Google provides a comprehensive suite of digital tools and solutions to support education. From interactive learning platforms to collaboration tools, Illuminate focuses on enhancing the educational experience for both students and teachers. It integrates various Google offerings such as Google Workspace, Classroom, and other innovative technologies to create an engaging and efficient learning environment.

TestMatic AI is an AI-driven platform designed to convert your notes, documents, audio, and video files into personalized practice tests and flashcards. With AI-powered question generation, you can easily generate custom test questions, allowing you to focus on studying. Save valuable time and elevate your learning experience through targeted and high-quality practice questions. Ideal for students, teachers, and professionals.

This AI-powered tool acts as a study companion that enhances your academic journey. By providing instant answers and detailed solutions, Transcript helps you tackle complex concepts easily. Whether you're studying for exams or managing daily homework, this extension aims to optimize your learning process by offering deep insights across various subjects. Its user-friendly interface allows students to navigate effortlessly, ensuring that your study sessions are both efficient and productive.

ThetaWave AI is a comprehensive educational tool designed to make learning more engaging and efficient. It captures lecture content in real-time or processes uploaded documents to convert them into well-structured notes. These notes can be further transformed into mindmaps, quizzes, flashcards, and even personalized podcasts. With support for various formats, including PDFs, Word documents, and videos, ThetaWave ensures that every piece of content is converted into an easy-to-study format, making it the ultimate study buddy for students.

The GA-based NQueen Solver uses a modular 2APL multi-agent architecture where each agent encodes a candidate N-Queen configuration. Agents evaluate their fitness by counting non-attacking queen pairs, then share high-fitness configurations with others. Genetic operators—selection, crossover, and mutation—are applied across the agent population to generate new candidate boards. Over successive iterations, agents collectively converge on valid N-Queen solutions. The framework is implemented in Java, supports parameter tuning for population size, crossover rate, mutation probability, and agent communication protocols, and outputs detailed logs and visualizations of the evolutionary process.