Adapting data science competencies by role and purpose: Voice AI

TL;DR

This paper shows how to adapt data science skills for different roles in voice AI, making learning more practical and collaborative.

Contribution

A persona-inductive method to adapt data science competencies for diverse roles in voice AI education.

Findings

Competency adaptation is feasible and practical in fast-changing AI fields.

Cross-role collaboration is essential for effective learning in voice AI.

Frameworks were successfully implemented in a multi-institutional summer school.

Abstract

Competencies help define the skills and knowledge needed by learners. Often broad, educators integrate competencies to provide a framework for curricula or professional standards. For data science, the rate of change in the field, role variations, and specificity in key applications can be challenging. Our objective was to adapt general data science competencies for different learner roles in an emerging area: the clinical utility of Voice, Language, and Speech-based Artificial Intelligence/Machine Learning (AI/ML). Using a persona-inductive approach, we adapted competencies to support learners from varying professional and educational backgrounds and implemented these adaptations in a multi-institutional summer school. Results from these pilot efforts demonstrated feasibility, highlighted the importance of cross-role collaboration, and provided lessons for scaling to broader audiences. Our frameworks show that competency adaptation is necessary and practical in rapidly evolving AI domains.