MOON Framework: An Emotionally Adaptive Voice Interaction Model for Older Adults

As the world experiences a rapidly aging population, it has become a pressing design challenge to ensure that emerging digital technologies remain understandable, supportive, and meaningful for older adults. One of the most promising modalities for promoting inclusive interaction is the Voice User Interface (VUI), supported by artificial intelligence (AI) and speech recognition. However, most existing VUIs emphasize functional accuracy and overlook users’ emotional conditions, such as anxiety, confidence, or frustration, which significantly affect long-term engagement and adoption. To address this gap, this study introduces a structured and adaptive model for developing emotionally intelligent voice interfaces, namely MOON Framework (Model-Observation-Optimization-Nurture). This framework integrates demographic and linguistic profiling (Model), real-time emotional perception (Observation), adaptive vocal modulation (Optimization), and feedback-driven learning (Nurture) to create a closed-loop system capable of adjusting dynamically to user experience. Grounded in Affective Computing Theory, Socioemotional Selectivity Theory (SST), and Adaptive User Modeling, MOON Framework conceptualizes empathy as a measurable and computable dimension of VUIs. Unlike existing affective systems that rely primarily on visual or physical expressiveness, MOON demonstrates that vocal empathy, achieved through modulation of tone, cadence, and speech rate can foster comparable emotional attunement. Its cyclical design transforms emotion from a passive observation into an active driver of system adaptation. Focusing on familiarity and confidence specifically in older adults, MOON provides both a theoretical foundation and a practical framework for creating emotionally inclusive AI technologies that promote trust, engagement, and long-term well-being.

@article{Sagga2025,
  title     = {MOON Framework: An Emotionally Adaptive Voice Interaction Model for Older Adults},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Hasan Sagga and Richard Stone},
  doi       = {10.14569/IJACSA.2025.0161185},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161185}
}