Developing AI Agents for Disability and Self Determination [Testμ 2026]

At Testμ Conf 2026, University of Kansas professor Lisa Dieker discussed Project RAISE, a concluded $2.5 million U.S. Department of Education-funded initiative that used a closed-loop AI agent, ZB, to help third-grade-and-older students identified with autism learn robot coding, communication, and self-regulation. Central to her presentation was co-design: after children rejected the researchers’ original agent design, about 1,000 students voted on alternatives, with greater weighting for autistic students, while the agent’s language and prompts were drawn from recordings of how children actually spoke during coding activities. The project avoided facial and emotion tracking after vendors could not clarify whether their models represented people with disabilities, instead using simpler presence cues, and used heart-rate data to increase supportive prompts during elevated stress without offering ungrounded performance praise. Dieker reported improvements in communication, on-task behavior, and mathematics practices from a final randomized study involving 10 schools and 150 students, although no study citation or effect sizes were provided. She argued that accessibility compliance alone does not ensure practical usability, urging developers to include disabled people from the outset, design agents to fill specific gaps rather than replace learning or autonomy, and apply particular caution to autonomous systems, biometrics, privacy, and users with multiple disabilities.

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