A Pedagogy for Embedding Artificial Intelligence and Technology across CBGL MBA Programs for Future Workforce Preparedness

Artificial intelligence is rapidly transforming education and technology (Zhai et al., 2021). However, the education industry—especially business education outside computer science and engineering—faces significant challenges in integrating AI into curricula. Addressing these challenges is crucial for training an AI-ready workforce, as companies will require employees who understand AI concepts to manage and collaborate with AI implementation agents.

Due to the scarcity of curriculum development frameworks and pedagogical resources as well as the disciplinary associations with regulatory and accreditation frameworks such as AACSB and TEQSA in Australia, business schools are facing the challenge of how to effectively incorporate AI into curriculum design to enable business students to remain relevant in AI era (Xu & Babaian, 2021). Thus, Business schools have adopted various approaches to integrate AI into their curricula. These range from relying on case studies, which may have limited impact on developing AI skills, to introducing programming and technical skills, which can sometimes be superficial. However, there is potential for improvement around applying best practices in designing a curriculum that adequately prepares business students with the AI skills needed to thrive in the complex future of work (Chen et al., 2021). There has also been calls for pedagogical evolutions to incorporate AI-driven competencies that can support the current and emerging demands in the industry (Allil, 2024).

The purpose of the current study is to develop a framework for integrating AI in business programs informed by current research and job market insights for best practice in curriculum design. To this end, we are utilising a 3-phased approach which is discussed in the method section below.

To address this research problem, we adopted a three-phase approach: Phase I (current study): a bibliometric analysis, Phase II: a systematic literature review (research in progress), and Phase III: an analysis of changes in job listings on LinkedIn for various business roles over the past three years by applying natural language processing models to extract insights.

The bibliometric analysis, shown in Figure 1, using the terms “curriculum,” “business,” and “artificial intelligence” in the Scopus database, identified three main themes: Industry, Concept, and Engineering. The Industry theme focuses on trends like digital transformation and future requirements for employability of future business graduates, reflecting industry needs.

The Concept theme centres on course design, assessment, and learning, and how to develop curriculum that is aligned with the current trends in the discipline as well as the needs of the job market.

The Engineering theme highlights the need to incorporate technical aspects of working with artificial intelligence in business practices and how those skills are incorporated in business curricula to produce AI-proficient graduates.

The ongoing second phase (systematic literature review) involves a deeper review of each theme, with the final phase comparing these insights to recent job postings and using topic modelling to identify research gaps.

As such, in this presentation, we present the results of our research along with our proposed research-informed plan to incorporate AI in the MBA programs at Flinders University by taking into consideration key elements of the programs such as learning and teaching, curriculum development and design, assessment strategies, learning outcomes, research teaching nexus, graduate competencies, work-integrated learning, and industry projects