BAACk to the Future with AI

Feb 11, 2025

As technological advancements continue to develop, artificial intelligence (AI) holds the potential to transform industries and improve lives to what once seemed futuristic. AI is destined to revolutionize AAC in ways that enhance accessibility, personalization, efficiency, and effectiveness for individuals with complex communication needs.

AI can deliver deeply personalized communication experiences when using AAC. AI-powered AAC systems can analyze patterns in an individual's language, preferences, and communication styles to suggest words or phrases proactively (Sennott et al., 2019). This means that AAC systems could predict what an individual may want to say next, significantly reducing the time and energy needed to construct messages.

For instance, a child using an AI-powered AAC system might have certain phrases they frequently use during classroom activities. AI could recognize these patterns and prioritize those phrases during school hours, creating a smoother and more natural communication flow.

AI can leverage the predictive features of robust language systems in AAC systems by learning from the individual’s previous selections and adapting to their evolving communication needs and skills.

Advanced machine learning models could predict the most relevant language in real time, streamlining communication and reducing fatigue (Sennott et al., 2019).

AI’s natural language processing capabilities can break language barriers. AAC systems equipped with AI could offer real-time translation, enabling individuals with complex communication needs to communicate effectively in different languages (Di Paola et al., 2024).

This feature can foster greater inclusion and independence for all individuals in multicultural environments.

Histrionically, the development of synthetic voices has focused on clarity and functionality. However, AI now allows for the creation of highly personalized voices. By analyzing minimal audio input from a family member or an individual’s vocalizations, AI can generate a unique voice that reflects personality, age, ethnicity, and regional accent.

This not only fosters a sense of identity for individuals with complex communication needs, but it also strengthens interpersonal connections by making their voice relatable and authentic.

Beyond enhancing user experience of AAC, AI can support AAC teams by offering predictive analytics. By analyzing data from AAC usage, AI could provide concrete and objective insights into progress, identify areas needing intervention, and even suggest tailored strategies for implementation (Cai et al., 2023).

This data-driven approach can help speech-language pathologists and other team members optimize AAC use and outcomes in individuals with complex communication needs.

Although the future of AI in AAC is thrilling, it is essential to consider ethical considerations and potential challenges.

Data privacy, for instance, must be a top priority when implementing AI-powered AAC systems. Individuals with complex communication needs and their families need assurance that their data will be handled securely and responsibly.

Moreover, ensuring that AI-powered AAC systems are inclusive and unbiased is crucial. Developers must consider diverse cultural and linguistic contexts to avoid perpetuating biases or excluding certain groups of individuals. Inclusion in terms of affordability and ease of use will also play a key role in making AI advancements available to all AAC stakeholders.

The integration of AI into AAC technology holds infinite potential to empower individuals with complex communication needs. Collaboration between technologists, speech-language pathologists, and individuals with complex communication needs will be vital to ensure that AI innovations in AAC tools are meaningful, ethical, and accessible for all. By enhancing personalization, improving efficiency, and breaking down communication barriers, AI is paving the way back to a once futuristic AAC industry where communication is effortless, seamless, and individualized.

References

Cai, S., Venugopalan, S., Seaver, K., Xiao, X., Tomanek, K., Jalasutram, S., Morris, M. R., Kane, S., Brenner, M. P., Nelson, P. Q., Gleason, S. M., Casey, B., Vance, D., Komman, E., McDonald, R. L., & Narayanan, A. (2023). Using Large Language Models to Accelerate Communication for Users with Severe Motor Impairments. arXiv, 1–28. https://doi.org/https://doi.org/10.48550/arXiv.2401.08866

Di Paola, A., Muraro, S., Marinelli, R., & Pilato, C. (2024). Foundation Models in Augmentative and Alternative Communication: Opportunities and Challenges. arXiv, 1–8. https://doi.org/10.48550/arXiv.2401.08866

Sennott, S. C., Akagi, L., Lee, M., & Rhodes, A. (2019). AAC and Artificial Intelligence (AI). Topics in Language Disorders, 39(4), 389–403. https://doi.org/10.1097/tld.0000000000000197

Hannah Foley

Empowering Teams of Those With Complex Communication Needs Through Support and Implementation