This study integrates critical AI scholarship with relational communication theories to explain how AI language modifications shape the quality of government-citizen communication. Distinguishing between informational-cognitive quality (clarity, ease of response) and expressive-constitutive quality (politeness, respectfulness, feeling heard, trust, urgency, empathy), we hypothesize that AI yields uncontested benefits for the former but contested effects for the latter, potentially enhancing relational markers while muting authentic emotional cues. Using a vignette-based survey with 220 citizens and 214 civil servants in China, we assess perceptions across five interaction contexts: service requests, policy inquiries, complaints, suggestions, and emergencies. Results from paired t-tests and mixed-effects regressions support the claim that AI enhances both informational-cognitive and expressive-constitutive quality from the perspectives of citizens and civil servants, with significant improvements in clarity, politeness, satisfaction, trust, and empathy, but provide no consistent evidence of urgency or empathy signals. These findings suggest that concerns over algorithmic emotional flattening may be overstated or context-specific; they offer theoretical insights into AI-mediated public interactions and practical implications for fostering trust and efficiency in digital governance.

翻译：本研究整合批判性人工智能研究与关系沟通理论，以阐释AI语言修饰如何影响政府与公民的沟通质量。通过区分信息认知质量（清晰度、回应便捷性）与表达建构质量（礼貌性、尊重度、被倾听感、信任度、紧迫性、共情力），我们提出假设：AI对前者产生无争议的改善效果，而对后者则存在争议性影响——可能提升关系指标的同时弱化真实情感信号。基于中国220名公民与214名公务员的情景式问卷调查，我们评估了五种互动情境下的感知差异：服务请求、政策咨询、投诉、建议及紧急事件。配对t检验与混合效应回归分析结果表明，从公民与公务员的双重视角来看，AI能同时提升信息认知质量与表达建构质量，在清晰度、礼貌性、满意度、信任度及共情力方面均有显著改善，但未发现对紧迫性或共情信号的稳定增强证据。这些发现表明，对算法情感扁平化的担忧可能被夸大或具有情境特异性；研究为AI中介的公共互动提供了理论洞见，并为数字治理中信任构建与效率提升提供了实践启示。