Abstract
Empathy-aware interaction is becoming a core requirement as AI systems mediate healthcare, education, and public services. For software teams, the challenge is not “make the bot nice,” but: how do we design and ship systems that sense user context, adapt with calibrated empathy, and still remain private, auditable, and predictable?
This article proposes a practice-oriented framework that unifies affect sensing, context modeling, policy-based adaptation, and transparent explanations into a deployable pipeline for human–machine systems. The contributions for developers are:
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