Toward an Integrative Professional and Personal Competency-Based Learning Model for Inclusive Workforce Development

Abstract

Recent workforce disruptions highlight the need for just-in-time competency acquisition. Developing cyber-human tools that incorporate both human guidance and artificial intelligence may shorten learning and provide better career-upskilling pathways. Deconstructing degree programs to provide adaptive pathways of multi-modal micro-experiences offers greater flexibility. To implement such learning programming, the Calhoun Discovery Program (CDP) at Virginia Tech and its industry and non-profit partners are developing an adaptive education model based on Integrative Professional and Personal Competencies (IPPCs) for Industry 4.0. We argue that implementing whole-person-development-focused curricula that uses heterogeneous analytics and adaptive pathways can increase learners' mobility within current and future economies. With our partners, we have developed real-world applied problem solving experiences to prepare transdisciplinary learners to work collaboratively on Industry 4.0 applications promoting sustainable and equitable development. This paper defines IPPCs and elaborates how they are integrated in the CDP through Problem-based Learning Experiences (PBLE), research and just-in-time modules. We note program outcomes over the first two years or operations and the generalizable takeaways of IPPC-based learning. Next, we describe computer-assisted tools we will develop to help us standardize and scale this learning model and summarize what the learning cycle looks like in our model. We conclude by sketching prospects for scaling this approach to K-12, industry and other settings.

Keywords

• prior learning assessment
• machine learning
• problem-based learning
• artificial intelligence