This systematic literature review (SLR) aims to comprehensively examine previously published articles on the drivers of artificial intelligence (AI) to identify the key drivers and develop a future agenda for AI in business education.
In this study, using The Population, Intervention, Comparison, and Outcome (PICO) and Preferred Reporting Items For Systematic Reviews and Meta-Analyses (PRISMA) protocols, we selected 35 published articles, and the researchers employed antecedents, decisions, and outcomes (ADO) framework was employed for the final synthesis.
The 19 most relevant AI drivers were identified for business education. However, five of those (technology selection for the classroom, technical support for teachers and students, common student-teacher perceptions of AI, students’ and teacher's prior experience of using AI, the teachers’ and students’ non-technical AI-related skills) were identified as antecedents. Six of those (implementation of existing AI technology, teachers' pedagogical selection, ethical considerations for using AI, evaluation mechanisms, human–AI collaboration design, and culturally responsive AI education) were identified as decision factors. The remaining eight (adaptive learning systems and personalization, assessment and evaluation enhancement, intelligent tutoring systems, readiness for AI-sensitive new business roles, profiling and prediction, enhanced understanding of AI learning, curriculum innovation and new business programs, and tracking students' health and well-being) were identified as the outcomes of AI drivers for business education.
Through the lens of practitioners, our suggested framework not only describes the major drivers of AI in business schools, but also highlights the key areas where business schools will need the cooperation of business institutions, as cooperation between academia and industry will be critical for implementing AI in business schools.
Most of the previous studies focus on pedagogical or technological aspects in isolation. However, this study provides a holistic view, linking behavioral, institutional, and technological antecedents (A) to decision-making processes (D) and their outcomes (O).
