AI-Based Smart Assistants: A Review Of Natural Language Understanding, Task Automation And Multimodal Interaction

Abstract—Smart assistants help users complete everyday tasks such as answering questions, searching information, setting reminders, checking the weather, opening applications and controlling devices through natural voice or text commands. This paper reviews the technologies that make such assistants possible and the way they have evolved from rule-based chatbots to systems built on large language models that can call external tools. The review covers input processing through speech recognition and text normalization, natural language understanding with intent classification and slot filling, dialogue management, task execution through application programming interfaces and operating-system commands, and response generation through text and speech synthesis. Retrieval-augmented generation and tool-use methods that ground assistant answers in current information are examined, along with personalization and memory. A modular architecture suitable for implementation in Python is proposed, covering the interaction, understanding, reasoning, skills and integration layers. A research gap analysis, objectives, a comparison of assistant design paradigms and a discussion of challenges in accuracy, connectivity, privacy and multilingual support are presented. The review concludes that combining a reliable task-oriented core with a language-model reasoning layer offers an efficient, user-friendly and extensible assistant for education, healthcare, business and home automation.