The rapid evolution of computing technologies continuously reshapes hands-on learning experiences, requiring students to adapt to ever-changing tools, frameworks, and best practices. Traditional educational materials, such as textbooks, lecture slides, and static documentation, often fail to keep pace with these changes, leaving students reliant on step-by-step guides that may quickly become outdated due to updates in software libraries, hardware requirements, or cloud service configurations. As a result, students often encounter issues where instructions no longer match the latest versions of tools, making troubleshooting an essential skill. However, limited instructor availability in large classrooms or online courses makes it difficult to provide immediate guidance, leading to inefficiencies in problem-solving. Artificial Intelligence (AI)-powered tutoring solutions have been explored to support learning, but most existing models lack contextual awareness, often providing generic or incomplete responses that do not reflect the constantly changing technical landscape.

This paper presents a context-aware AI assistant based on Retrieval-Augmented Generation (RAG) to address these challenges by dynamically retrieving relevant and up-to-date technical information before generating responses. Unlike traditional AI tutors, which rely solely on pre-trained knowledge, this system searches structured learning resources, documentation, and troubleshooting guides to provide precise, contextually relevant explanations tailored to students' real-time queries. By integrating real-time retrieval mechanisms with AI-powered guidance, the assistant adapts to rapid technological changes, ensuring that students receive accurate, step-by-step support even when libraries, dependencies, or configurations evolve. This reduces cognitive load, allowing students to focus on problem-solving rather than spending time searching for solutions. Additionally, the system provides real-time troubleshooting assistance, helping learners navigate issues related to software environments, hardware compatibility, and deployment configurations, all of which frequently shift due to technological advancements.

To further examine its applicability, the paper explores possible architectures for implementing a RAG-based AI assistant, analyzing the trade-offs between different configurations in terms of computational cost, scalability, and integration complexity. Factors such as cloud-based versus on-premise deployment, vector database choices, Application Programming Interface (API)-based retrieval, and Large Language Model (LLM) hosting strategies are examined to determine the feasibility of deploying such a system in different educational settings. By considering both technical implementation and cost implications, this work provides a comprehensive perspective on the challenges and opportunities of integrating AI-driven contextual support in computing education, offering insights into the most effective approaches for enhancing hands-on learning experiences.

Keywords: Artificial Intelligence, Interactive Education, Retrieval-Augmented Generation.