Abstract

Emotion‑aware computing refers to systems designed to detect, interpret, and respond to human emotional states with the goal of enhancing personalization and adaptivity in digital experiences. By integrating affective signals from users—such as facial expressions, voice intonation, physiological responses, and behavioral cues—with computational models, such systems can tailor content delivery, interaction modalities, and feedback mechanisms according to users’ moment‑to‑moment affective needs. Emotion‑aware models are increasingly relevant across domains including education, entertainment, healthcare, human–robot interaction, and e‑commerce, potentially improving user engagement, satisfaction, and task performance. This research investigates foundational theories underlying emotion recognition, computational modeling techniques, and adaptive user‑centered design strategies that leverage emotional context. Through systematic synthesis of literature, critical evaluation of modeling approaches, and comparative analysis of real‑world applications, we highlight both the strengths and limitations of current emotion‑aware computing frameworks. We also explore ethical considerations in affective sensing and discuss challenges related to data quality, cultural variance, privacy, and real‑time inference. Empirical evidence suggests that emotion‑aware systems can significantly enhance personalization and responsiveness, yet there remain open challenges in accuracy, generalizability, and user trust. We conclude by proposing a research agenda focusing on multimodal fusion, transparent affective models, and user‑centric ethical safeguards.