In the endeavor to advance industrial engineering and management (IEM) education, this research underscores the imperative of supporting a dynamic and responsive adaptation of a competency-based curriculum (CBC) to meet the demands of an ever-evolving industrial landscape and job market. Our study contributes to competency-based education (CBE) by demonstrating how Artificial Intelligence (AI) can inform the definition of a CBC in the IEM field, thus initiating the pioneering steps towards a collaborative human-AI approach in CBC design. Through a stepwise methodology based on semantic analysis, text mining, natural language processing (NLP) models, informetrics approaches, and clustering algorithms, we provide data-driven insights to inform the curriculum development process. This approach enabled us to identify educational gap, particularly in domains such as digital twin engineering and human-centric IEM. Moreover, this study advocates for higher education institutions (HEIs) to embrace a more structured and collaborative approach to continuously developing competency-based curricula. In this perspective, AI (including generative AI) emerges as a valuable ally in curriculum design. This approach proves instrumental in crafting competitive and appealing curricula, especially at peripheral universities. This study culminates in an updated WING model showing how to build Industry 5.0 related curricula and a series of recommendations for engineering educators.