AI对话正催生一种更易被机器理解的英语新语体。
Machine-Facing English: Defining a Hybrid Register Shaped by Human-AI Discourse
- 通过人机交互观察，发现语言趋向语法僵化、表达简化
- 五种特征提升机器解析准确率，但压缩了语言表现力
- 适合研究人机交互设计与多语言用户教学者参考
机器面向英语（MFE）是一种因人类语言适应人工智能交互而兴起的新语体。基于话语分析理论，本研究通过双语（韩英）语音与文本产品测试中的定性观察，结合人工校准的自然语言声明式提示（NLD-P）反思性写作，揭示持续人机互动如何使语言趋于语法刚性、语用简化与过度明确化，从而增强机器可读性，却牺牲自然流畅性。主题分析识别出五类典型特征：冗余清晰性、指令式句法、受控词汇、扁平语调和单一意图结构，这些特征虽提高执行准确性，却限制表达丰富性。MFE的演进凸显沟通效率与语言丰富性间的持续张力，对对话界面设计及多语言用户教育提出挑战。研究强调需加强方法论披露并开展未来实证验证。
原文摘要 · Abstract (English)
Machine-Facing English (MFE) is an emergent register shaped by the adaptation of everyday language to the expanding presence of AI interlocutors. Drawing on register theory (Halliday 1985, 2006), enregisterment (Agha 2003), audience design (Bell 1984), and interactional pragmatics (Giles & Ogay 2007), this study traces how sustained human-AI interaction normalizes syntactic rigidity, pragmatic simplification, and hyper-explicit phrasing - features that enhance machine parseability at the expense of natural fluency. Our analysis is grounded in qualitative observations from bilingual (Korean/English) voice- and text-based product testing sessions, with reflexive drafting conducted using Natural Language Declarative Prompting (NLD-P) under human curation. Thematic analysis identifies five recurrent traits - redundant clarity, directive syntax, controlled vocabulary, flattened prosody, and single-intent structuring - that improve execution accuracy but compress expressive range. MFE's evolution highlights a persistent tension between communicative efficiency and linguistic richness, raising design challenges for conversational interfaces and pedagogical considerations for multilingual users. We conclude by underscoring the need for comprehensive methodological exposition and future empirical validation.
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