I, Robot? Exploring Ultra-Personalized AI-Powered AAC; an Autoethnographic Account

Generic AI auto-complete for message composition often fails to capture the nuance of personal identity, requiring significant editing. While harmless in low-stakes settings, for users of Augmentative and Alternative Communication (AAC) devices, who rely on such systems for everyday communication, this editing burden is particularly acute. Intuitively, the need for edits would be lower if language models were personalized to the communication of the specific user. While personalization has been shown to be technically feasible, it raises questions about how such systems affect AAC users' agency, identity, and privacy. To understand how these shifts in practice, we conduct 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. Observations across these phases include that logging everyday conversations reshaped the author's sense of agency, the model training selectively amplified or muted aspects of their identity, and suggestions occasionally resurfaced private details outside their original context. Our findings show that ultra-personalized AAC reshapes communication by continually renegotiating agency, identity, and privacy between user and model. We highlight design directions for building context-adaptive, user-controlled personalization AAC technology that supports expressive, authentic communication.