Toward Empathic Adaptive Interaction Design: A Taxonomy for Inclusive and Neuroadaptive UX/UI Systems

Human–computer interaction (HCI) has traditionally relied on assumptions of a relatively uniform user. Neurodiversity challenges this premise by highlighting variation in perception, attention, communication, and sensory processing. Advances in multimodal sensing, artificial intelligence (AI), and adaptive interfaces increasingly enable systems to respond to changing user needs in real time. This paper proposes an evidence-informed taxonomy for empathic adaptive interaction design, developed through a structured interdisciplinary review and conceptual synthesis. The taxonomy comprises six dimensions: User Cognitive Profile, Empathic Inference Capability, Sensing Modality, Adaptation Strategy, Application Context, and Governance, Autonomy, and Ethics. Together, these dimensions provide a structured framework for analyzing how systems sense user state, infer support needs, adapt interaction, and maintain transparency and user control. The framework supports the systematic analysis, comparison, design, evaluation, and future empirical validation of inclusive, neuroadaptive, and multimodal interaction systems responsive to cognitive diversity.