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AI-enabled AT Framework: a principles-based approach to emerging assistive technology.

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Summary
This summary is machine-generated.

A new framework aids decision-making for Artificial Intelligence (AI)-enabled Assistive Technology (AT). This co-designed tool ensures ethical, user-centered AI applications for people with disabilities, promoting informed development and assessment.

Keywords:
Artificial intelligenceassessmentassistive technologydisabilityframework

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Area of Science:

  • Assistive Technology (AT) and Artificial Intelligence (AI)
  • Disability Studies
  • Human-Computer Interaction

Background:

  • Assistive Technology (AT) supports individuals with disabilities in daily tasks.
  • Artificial Intelligence (AI) is increasingly integrated into AT development.
  • A structured approach is needed for informed decision-making in AI-enabled AT.

Purpose of the Study:

  • To present the AI-enabled AT Framework, a tool for effective decision-making, development, and assessment of AI-enabled AT.
  • To facilitate informed choices and control for people with disabilities in the AI-enabled AT space.
  • To support stakeholders in navigating the complexities of AI in AT.

Main Methods:

  • Co-designed framework using a participatory research approach.
  • Engaged diverse stakeholders: people with disabilities, carers, industry, government, and researchers.
  • Employed a multi-stage process including literature review, interviews, focus groups, and workshops.

Main Results:

  • The AI-enabled AT Framework offers a structured, person-centered approach for assessing AI-enabled AT.
  • Incorporates six core domains: user experience, privacy, security, quality, safety, relative value, and human rights.
  • Provides clear evaluation criteria and highlights the importance of ongoing stakeholder engagement.

Conclusions:

  • The AI-enabled AT Framework is a practical tool for enhancing decision-making in AI-enabled AT development, evaluation, and deployment.
  • Emphasizes co-design and stakeholder engagement for ethical, effective, and user-centered AI applications.
  • Recommends future research on framework validation, implementation, and emerging adoption challenges.