Journal of AI-Driven Communication Engineering

Special Issue: Artificial Intelligence Across the Communication Stack: Engineering, Human Interaction, and Governance in the 6G Era

Vol. 2 (2026)

Affective Dimensions of Trust in AI-Mediated Human Communication: Toward an Emotionally Intelligent Design Framework for Conversational Agents

DOI:
https://doi.org/10.31875/2979-1081.2026.02.08
Submitted
July 5, 2026
Published
2026-07-05

Abstract

As large language model (LLM)-based conversational agents become embedded in everyday communication contexts, the affective dimensions of user-AI interaction have emerged as a critical engineering and governance concern. While cognitive trust is usually associated with perceived accuracy, reliability and transparency, affective trust refers to the emotionally mediated sense of comfort, warmth and relational security that users experience when interacting with an AI system. This paper examines the theoretical foundations of affective trust in human-AI communication and proposes the Affective Trust Engineering (ATE) framework as a structured design approach for emotionally intelligent conversational agents. In response to implementation-oriented gaps in the literature, the revised framework links affective trust dimensions to concrete AI communication system components, including affect detection, context modelling, prompt and policy orchestration, response generation, safety gating, audit logging and continuous evaluation. The paper also introduces a conceptual implementation diagram, a short research approach section, real-world application examples, and a completed reference base grounded in recent human-AI interaction, affective computing and AI governance literature. The central argument is that affective trust should be designed as a calibrated, bounded and measurable system output rather than treated as a diffuse by-product of pleasant interaction.

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