Toward Emotionally Intelligent AI Systems: Foundations, Challenges, and the Road Ahead

Artificial Intelligence (AI) systems are increasingly used in settings in which their outputs alter decisions, communication, and access to services. In such settings, technical accuracy does not exhaust the conditions of a satisfactory interaction, because a user's emotional state may affect both interpretation and outcome. This paper examines the theoretical foundations of Emotional Intelligence (EI), the emergence of affective computing, and the present capabilities of emotionally aware AI across applied contexts. It brings together evidence from psychology, computer science, human-computer interaction, and ethics to ask a narrower question: which affective capacities can be implemented reliably, where do present systems fail, and which failures matter most for human-facing deployment? The review identifies technical and ethical constraints, organizes the most consequential research gaps, and proposes priorities for future development. Its central argument is that, in consequential human-machine interaction, systems should be able to identify affective cues, interpret them in context, and modulate their responses without mistaking statistical regularity for human understanding. Emotional competence, so understood, is not an ornamental addition to AI but one condition of socially useful and ethically defensible performance.