When modernization and other changes demand workforce reskilling, employers often turn to local colleges for training programs. Doing so can be a frustrating experience. HR and talent professionals have difficulty identifying and communicating requirements, especially for new jobs and roles, while college continuing education (CE) and professional development offices have difficulty understanding and responding to company needs. This article describes an NSF Convergence Accelerator project called SkillSync™ in which multiple forms of AI are used to address this specific problem and provide national efforts (e.g., the US Chamber of Commerce Talent Pipeline Management initiative) with skills data and skills alignment services. Skillsync uses variations on the Siamese Multi‐depth Transformer‐based Hierarchical Encoder (SMITH) and other natural language understanding methods to map job descriptions and course information to skills taxonomies, uses machine‐learned models to align skills needs with learning outcomes and training, and incorporates an intelligent coach based on Georgia Tech's Jill Watson “virtual teaching assistant” to answer questions about Skillsync's vocabulary, functionality, and process. This article describes these AI methods, how these methods are used in Skillsync, and the challenges involved.

In 2015, the American Association of Veterinary Medical Colleges (AAVMC) developed the Competency-Based Veterinary Education (CBVE) framework to prepare practice-ready veterinarians through competency-based education, which is an outcomes-based approach to equipping students with the skills, knowledge, attitudes, values, and abilities to do their jobs. With increasing use of health informatics (HI: the use of information technology to deliver healthcare) by veterinarians, competencies in HI need to be developed. To reach consensus on a HI competency framework in this study, the Competency Framework Development (CFD) process was conducted using an online adaptation of Developing-A-Curriculum, an established methodology in veterinary medicine for reaching consensus among experts. The objectives of this study were to (1) create an HI competency framework for new veterinarians; (2) group the competency statements into common themes; (3) map the HI competency statements to the AAVMC competencies as illustrative sub-competencies; (4) provide insight into specific technologies that are currently relevant to new veterinary graduates; and (5) measure panelist satisfaction with the CFD process. The primary emphasis of the final HI competency framework was that veterinarians must be able to assess, select, and implement technology to optimize the client-patient experience, delivery of healthcare, and work-life balance for the veterinary team. Veterinarians must also continue their own education regarding technology by engaging relevant experts and opinion leaders.

When modernization and other changes demand workforce reskilling, employers often turn to local colleges for training programs. Doing so can be a frustrating experience. HR and talent professionals have difficulty identifying and communicating requirements, especially for new jobs and roles, while college continuing education (CE) and professional development offices have difficulty understanding and responding to company needs. This article describes an NSF Convergence Accelerator project called SkillSync™ in which multiple forms of AI are used to address this specific problem and provide national efforts (e.g., the US Chamber of Commerce Talent Pipeline Management initiative) with skills data and skills alignment services. Skillsync uses variations on the Siamese Multi-depth Transformer-based Hierarchical Encoder (SMITH) and other natural language understanding methods to map job descriptions and course information to skills taxonomies, uses machine-learned models to align skills needs with learning outcomes and training, and incorporates an intelligent coach based on Georgia Tech's Jill Watson “virtual teaching assistant” to answer questions about Skillsync's vocabulary, functionality, and process. This article describes these AI methods, how these methods are used in Skillsync, and the challenges involved.

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