ALGOVIZ: An Interactive Algorithm Learning Platform with Visualization and AI-Powered Tutoring

Understanding algorithms is a fundamental requirement in computer science education. However, students often find it difficult to trace algorithm execution using only textual explanations and static code examples. This paper presents ALGOVIZ, a web-based interactive algorithm-learning platform that combines step-by-step visualization, pseudocode highlighting, playback controls, customizable input arrays, execution metrics, and an AI-powered tutoring interface. The platform supports Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, Quick Sort, Linear Search, and Binary Search. Its frontend uses HTML, CSS, and JavaScript, while Python Flask provides backend services and mediates communication with the Groq API. The AI Tutor receives contextual information about the selected algorithm and its current execution state to generate relevant explanations. This paper describes the motivation, related work, system requirements, architecture, algorithm execution model, AI integration, security considerations, implementation, project schedule and an evaluation framework. The paper distinguishes implemented capabilities from proposed evaluation: no controlled user-study results or performance benchmarks are claimed without measured data. ALGOVIZ provides an integrated technical foundation for exploring how visualization and context-aware conversational assistance can support self-directed algorithm learning.