Data Structures and Algorithms (DSA) are the backbone of computer science education; still, traditional learning methods based on static code examples and slide-show lectures are ineffective in communicating the step-by-step dynamic process of algorithms. This drawback makes it difficult for students to form correct mental images of algorithmic processes, leading to passive learning and superficial understanding. Recent breakthroughs in Artificial Intelligence (AI), specifically Large Language Models (LLMs), open new avenues for developing intelligent and interactive learning environments that adapt to the needs of individual learners and allow multiple modalities of interaction. This paper introduces the conceptualization and design of AlgoVista, an AI-augmented algorithm learning platform that combines real-time visualizations of algorithms with adaptive explanations and interactive assessments using LLMs. The proposed system combines a MERN-stack web interface with Python algorithm execution, an LLM for context-dependent explanations, and a text-to-speech system for audio narration. As algorithms run step by step, the system dynamically produces plain-language explanations for each state transition and provides short-form personalized quizzes based on the learner's past interactions and mistakes. AlgoVista, by integrating deterministic algorithm execution, multimodal explanation, and learner-centric assessment in a unified framework, seeks to convert passive algorithm demonstrations into active learning experiences.The proposed architecture remedies the major shortcomings of existing visualization systems by allowing adaptability, interactivity, and continuous feedback, which help to facilitate conceptual understanding and learning outcomes in Data Structure and Algorithm education.