Abstract

Intelligent Digital Assistants (IDAs) are software agents designed to interact with users through natural language, perform tasks, and provide information or services autonomously. Recent advances in artificial intelligence — particularly in natural language processing, machine learning, and user modeling — have enabled IDAs to exhibit contextual awareness, memory, and personalized interaction, allowing more natural, adaptive, and human‑centric experiences. Contextual awareness refers to the ability of an assistant to understand and leverage situational, temporal, and user‑specific cues to interpret intent and respond appropriately; memory enables the assistant to retain and recall user preferences, past interactions, and relevant history over extended time horizons; personalized interaction tailors dialog strategies, recommendations, and task support to individual user characteristics, habits, and goals. This paper provides a comprehensive review of core techniques and frameworks underlying these capabilities, including context modeling, short‑ and long‑term memory architectures, reinforcement learning for personalization, and multi‑modal interaction. We present a structured research methodology for building and evaluating IDAs with contextual and personalized capabilities, discuss the advantages and disadvantages of prevailing approaches, synthesize empirical findings from benchmark studies and real‑world deployments, and outline avenues for future research. Emphasis is placed on user experience outcomes, ethical considerations, privacy, and long‑term adaptation.