Twentieth International Conference on Technology, Knowledge, and Society

Abstract

Credit for Prior Learning (CPL) serves as a vital tool within universities to acknowledge and reward the knowledge and skills gained beyond traditional educational settings. This extends to experiences in the workforce, military, or volunteer engagements. By valuing and validating non-institutional learning, CPL can potentially enhance college completion rates by reducing the financial and temporal burdens associated with degree attainment. This potential impact is particularly profound for individuals from historically underserved backgrounds. Despite its promise, CPL remains underutilized due to practical obstacles inhibiting its scalability. A primary challenge lies in the subjective evaluation of non-institutional learning by faculty who lack standardized guidelines and assessment criteria. Submitters often struggle to correlate their knowledge and skills with the prescribed learning outcomes of academic programs. This study delves into research investigating the application of algorithmic and generative artificial intelligence (AI) techniques to establish more inclusive and efficient CPL processes. We discuss the concept of ‘skillification,’ which employs algorithms to establish connections between a learner’s experiences and the competencies outlined in academic curricula, and how it might streamline both the preparation and assessment of CPL portfolios. We also share insights into the use of generative language algorithms which comprehend context, enabling them to parse interview content to extract relevant skills information. We highlight the distinctions between these methodologies and propose a synergistic integration of these approaches to create a comprehensive solution for narrating one’s skills within specific contexts. We consider the implications for designing an algorithmically-enhanced system with the potential to revolutionize CPL processes.