Bridging the AI literacy gap: mixed-methods evidence from employers on workforce readiness and curriculum priorities

The rapid integration of artificial intelligence (AI) in the workplace has widened the gap between what universities teach and what employers expect of new hires. This study examines that gap through a mixed-methods research-to-practice design grounded in employer perspectives. Three objectives guided the work: (a) identify gaps between undergraduate AI preparation and workplace requirements, (b) develop a workforce-informed AI competency framework from documented industry needs, and (c) identify workforce-driven priorities for an AI literacy curriculum. Data were collected in four sequential phases: a structured narrative review ( n = 56), industry focus groups and interviews ( n = 13), survey instrument development, and a convenience sample of predominantly U.S. employers recruited through professional networks ( n = 110). Open-ended survey responses were analyzed through hybrid inductive-deductive thematic analysis. Findings indicated that employer-defined AI readiness is fundamentally socio-technical: task-specific prompting and tool fluency must be coupled with professional communication, collaboration, human oversight, and judgment, soft skills that employers rated as highly as technical AI competencies. Employers also perceived recent graduates as underprepared, particularly in AI output verification and AI ethics. Within this sample, stronger governance emphasis correlated with deeper AI integration and higher self-reported efficiency, but the results are descriptive and do not support causal inference. Employers recommended an undergraduate curriculum organized around five employer-informed AI competencies, with AI output verification and responsible AI use as core outcomes. The study contributes a socio-technical conception of workplace AI readiness in which technical and human-centered competencies are interdependent rather than parallel.