Purpose: This study investigates the significant determinants that lead different work-field employees to develop intentions to enroll in educational programs that use artificial intelligence (AI). Venkatesh's (2000) technology acceptance model 3 (TAM3) is the foundation for the proposed research model in the current study, presenting a complete nomological network of technology adoption determinants (individual differences, system features, social influence and facilitating conditions). Design/methodology/approach: Quantitative questionnaires were distributed January 2024 digitally, using Google Form, to employees enrolled in educational programs in higher education institutions for the purpose of work-based learning in Egypt. 405 questionnaires were analyzed, using statistical package for social science, version-26. Findings: This study found that employees develop intentions for work-based learning with AI if the technology is useful and easy. Employees perceive AI as useful with subjective norms, job relevance, result demonstrability and quality. They perceive AI as easy based on their level of self-efficacy, computer anxiety and perception of control. Practical implications: AI when used by higher education institutions can lead to admired learning among employees as it improves access to education opportunities, develops intelligent tutoring systems, personalizes education processes, etc. Thus, AI in work-based learning programs should be encouraged for fruitful education strategies and interventions. Originality/value: This study contributes to the growing body of knowledge on AI in higher education, skills and work-based learning. A comprehensive investigation of the multifaceted drivers of AI adoption among employees seeking continuous learning can aid in the development of effective interventions to enhance technology adoption in education. The novelty lies in understanding how to endorse AI use in training courses for employees offered in higher education institutions.