EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling

Yujia Du

doi:10.47297/taposatWSP2633-456918.20250605

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EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling

6卷第5期

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EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling
EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling
School of Information Engineering, Xi'an Eurasia University, Shanxi, Xi'an, 710065, China
2025年6卷第5期页码：87-93
纸质出版日期：2025-05-19
DOI：10.47297/taposatWSP2633-456918.20250605
Accepted：

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(2025). EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling. 科技理论与实践（英文版）, 6(5), 87-93.

Du Yujia.EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling[J].Theory and Practice of Science and Technology,2025,06(05):87-93.
(2025). EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling. 科技理论与实践（英文版）, 6(5), 87-93. DOI： 10.47297/taposatWSP2633-456918.20250605.

Du Yujia.EmoMATE: A Multimodal Emotion-Aware AI Assistant with Trust-Adaptive Interaction Modeling[J].Theory and Practice of Science and Technology,2025,06(05):87-93. DOI： 10.47297/taposatWSP2633-456918.20250605.

摘要

Abstract

In recent years

the integration of multimodal interaction and affective computing has driven a paradigm shift in human-AI collaboration. This paper proposes EmoMATE

a novel affect-aware AI assistant framework that dynamically fuses visual

auditory

and linguistic modalities to perceive and respond to user emotions in real-time. Unlike traditional assistant systems that rely on static rule-based logic

EmoMATE leverages a hybrid transformer architecture with cross-modal attention alignment and a continual trust calibration module to co-model affective cues and trust dynamics. We introduce a trust-aware reinforcement learning strategy that adaptively adjusts the assistant's communicative behavior based on inferred user trust states. Through a longitudinal user study involving 96 participants across diverse contexts (productivity

emotional support

decision-making)

we demonstrate that affect-driven interaction significantly improves perceived competence

empathy

and trust retention. Our findings reveal that multimodal emotional responsiveness is not only critical for user engagement but also acts as a core driver for long-term trust formation in AI systems. This work bridges affective computing and trust modeling

providing new insights into the design of socially intelligent

emotionally adaptive AI assistants.

关键词

Keywords

references

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