Baby Bliss Bot - Inclusive Design Research Centre

The Baby Bliss Bot (BBB) project explores the use of AI tools to enhance communication for AAC (Augmentative and Alternative Communication) users who rely on Blissymbolics — a semantic symbol language without phonology, originally developed by Charles K. Bliss and first used as a communication bridge for children with physical disabilities at the Ontario Crippled Children’s Centre in 1971. Today, Bliss symbols remain a vital alternative for nonvocal individuals, such as those with cerebral palsy, who face challenges acquiring phonological language skills.

While the rapid advancement of AI — particularly Large Language Models — offers significant potential to improve societal engagement for nonvocal individuals, it also poses risks for communities underrepresented in training data. BBB is a community-led initiative that addresses this gap through an inclusive, co-design process, positioning AAC and Blissymbol users as active decision-makers. Through collaborative development of experimental prototypes, the project aims to uncover innovative communication solutions and produce insights that inform the creation of more equitable, advanced AI-based AAC systems for marginalized minority language users.

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The rapid advancement of AI - particularly Large Language Models (LLMs) - offers exciting possibilities to enhance communication for AAC users. The inclusive design project described in this paper, titled Baby Bliss Bot, is led by a group of AI programmers collaborating with AAC users as co-designers in an interdisciplinary initiative. It uses Blissymbolics in its first phase to explore how the LLMs of AI can be leveraged to support AAC users in learning language and in communicating meaningfully across contexts and dialogue types - at home, in school, at work and from inquiry and discovery, to humour, creativity and beyond. Blissymbolics was chosen as the GRS in Phase One because of its comprehensive language capabilities and affordances that facilitated a multi-tiered approach. Through collaborative experimentation, the project investigates not only how to make AI systems more inclusive for outliers and minorities, but also how to design alternative training methods and interfaces. The aim is to support context-aware, personalized expression that respects the individuality of each AAC user.