AI Driven Competency Gap Analysis Model for Continuous Professional Development in STEM Industries

Abstract: The rapid transformation of science, technology, engineering, and mathematics (STEM) industries has intensified the demand for agile, future ready professionals. Organizations now face the dual challenge of identifying competency gaps within their workforce and aligning training opportunities with emerging skills. Traditional professional development (PD) models are often static, generic, and unable to capture the dynamic nature of evolving STEM roles (Lent et al., 2017; Ainslie & Huffman, 2019; Bryson & Zimmermann, 2020). This paper proposes an AI driven competency gap analysis model that leverages accessible GPT-class language models to extract skills from employee records and industry role requirements, analyze competency gaps, and recommend targeted micro credentials for continuous professional development. Grounded in Social Cognitive Career Theory (SCCT) (Bandura, 1986; Lent et al., 2017) and building on established research demonstrating the impact of organizational support for development on workforce commitment (Tansky & Cohen, 2001; Chick & Vance, 2025), the framework utilizes GPT's natural language processing capabilities for comprehensive competency analysis and generates human-readable development plans aligned with identified skill gaps (Burke, 2002; Boud & Jorre de St Jorre, 2021). By connecting individualized skill gap insights to scalable learning solutions through an accessible, single-platform approach, this study offers a replicable model that contributes to workforce development theory and practice. The findings highlight how GPT-supported continuous professional development can drive skill relevance, employee engagement, and organizational retention in fast evolving technical fields while maintaining practical implementation feasibility.

Keywords: AI-driven competency analysis; GPT-based workforce development; accessible professional development systems; STEM skills gap analysis