Adapting data science competencies by role and purpose: Voice AI.

Area of Science:

• Data Science
• Artificial Intelligence (AI)
• Machine Learning (ML)

Background:

• General data science competencies provide a broad framework for education and professional standards.
• The dynamic nature of data science, diverse roles, and specific applications present challenges for competency development.
• Emerging AI/ML applications in clinical settings require specialized skill sets.

Purpose of the Study:

• To adapt general data science competencies for various learner roles in the context of clinical utility of Voice, Language, and Speech-based AI/ML.
• To develop practical frameworks for competency adaptation in specialized AI domains.

Main Methods:

• A persona-inductive approach was used to tailor competencies.
• Adaptations were implemented and tested in a multi-institutional summer school program.

Main Results:

• Pilot implementations demonstrated the feasibility of adapted competencies.
• Cross-role collaboration was identified as a critical factor for success.
• Lessons were learned for scaling these competency frameworks to larger audiences.

Conclusions:

• Competency adaptation is both necessary and practical for rapidly evolving AI domains.
• Tailored data science competencies are essential for effective learning in specialized AI fields like clinical AI/ML.
• The developed frameworks support learners from diverse professional and educational backgrounds.