面向机器的英语：定义由人机话语塑造的混合语体
Machine-Facing English: Defining a Hybrid Register Shaped by Human-AI Discourse
AI总结：
本研究基于语体理论定义了由人机交互塑造的面向机器的英语（MFE）语体，揭示了其提升机器解析度但压缩表达范围的五大特征，并指出其在界面设计与多语言教学上的挑战。
摘要：
面向机器的英语（MFE）是一种新兴语体，由日常语言适应日益增多的AI对话者而形成。本研究借鉴语体理论（Halliday 1985, 2006）、语体化（Agha 2003）、受众设计（Bell 1984）和互动语用学（Giles & Ogay 2007），追溯持续的人机交互如何使句法僵化、语用简化和过度明确表述常态化——这些特征以牺牲自然流畅性为代价来增强机器可解析性。我们的分析基于双语（韩语/英语）语音和文本产品测试环节的定性观察，并在人工策展下使用自然语言声明式提示（NLD-P）进行反思性起草。主题分析确定了五个反复出现的特征——冗余清晰度、指令性句法、受控词汇、扁平化韵律和单一意图结构——这些特征提高了执行准确性但压缩了表达范围。MFE的演变突显了交际效率与语言丰富性之间的持续张力，为对话界面带来了设计挑战，并为多语言用户带来了教学考量。我们最后强调需要全面的方法论阐述和未来的实证验证。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
