Generic AI auto-complete for message composition often fails to capture the nuance of personal identity, requiring editing. While harmless in low-stakes settings, for users of Augmentative and Alternative Communication (AAC) devices, who rely on such systems to communicate, this burden is severe. Intuitively, the need for edits would be lower if language models were personalized to the specific user's communication. While personalization is technically feasible, it raises questions about how such systems affect AAC users' agency, identity, and privacy. We conducted an autoethnographic study in three phases: (1) seven months of collecting all the lead author's AAC communication data, (2) fine-tuning a model on this dataset, and (3) three months of daily use of personalized AI suggestions. We observed that: logging everyday conversations reshaped the author's sense of agency, model training selectively amplified or muted aspects of his identity, and suggestions occasionally resurfaced private details outside their original context. We find that ultra-personalized AAC reshapes communication by continually renegotiating agency, identity, and privacy between user and model. We highlight design directions for building personalized AAC technology that supports expressive, authentic communication.

翻译：通用的AI消息撰写自动补全功能通常无法捕捉个人身份的细微差别，需要用户进行编辑。在低风险场景中这或许无害，但对于依赖此类系统进行沟通的增强与替代沟通（AAC）设备使用者而言，这种负担尤为沉重。直观而言，若语言模型能针对特定用户的沟通方式进行个性化定制，编辑需求将会降低。尽管个性化在技术上可行，但这引发了此类系统如何影响AAC用户自主性、身份认同与隐私的深层问题。我们通过三阶段自我民族志研究展开探索：（1）收集第一作者七个月的AAC全量沟通数据；（2）基于该数据集微调模型；（3）持续三个月每日使用个性化AI建议。研究发现：日常对话的记录重塑了作者的自主性认知；模型训练选择性地放大或抑制其身份特质；建议内容偶尔会在原始语境之外重现私人细节。研究表明，超个性化AAC通过持续重构用户与模型间的自主性、身份认同及隐私边界，从根本上重塑沟通模式。我们进一步提出支持表达性与真实性沟通的个性化AAC技术设计方向。