A Competency-Based Framework for Business Education Transformation via AI Literacy

Thesis Abstract

The rapid integration of artificial intelligence (AI) into business decision-making processes necessitates a transformative shift in business education to develop AI-literate graduates who can design, manage, and govern AI-enabled enterprises. Despite growing demand for AI competencies, business curricula remain disproportionately focused on traditional management theories with limited incorporation of AI literacy, analytics, ethics, and governance. This study investigates a competency-based framework that enables the transformation of business education through structured AI literacy to bridge the gap between academic preparation and industry needs. The aim is to develop and validate a comprehensive framework that delineates core AI-related competencies, instructional designs, assessment methods, and governance mechanisms for business curricula. Specific objectives are to (1) identify critical AI competencies required by modern enterprises across management, marketing, finance, and operations; (2) examine existing competency models and theoretical lenses, notably Competency-Based Education (CBE) and the Technology Acceptance Model (TAM) as applied to AI literacy; (3) develop a context-sensitive framework that aligns curriculum objectives with industry-based AI capabilities; (4) test the framework’s validity and reliability through expert validation and empirical pilot implementation in three business schools; and (5) propose an implementation roadmap with evaluation metrics for continuous curriculum improvement. A multi-stage mixed-methods design is employed. In the first stage, a Delphi panel of 18 experts comprising business educators, AI practitioners, HR leaders, and accreditation professionals is conducted to achieve consensus on AI competencies and instructional modalities. In the second stage, a cross-sectional survey of 1,200 final-year undergraduate and MBA students across six accredited business schools assesses perceived importance, self-efficacy, and readiness for AI-enabled roles, using a 5-point Likert scale. In the third stage, action research is implemented in two courses (one core, one elective) in each of the three partner schools over an academic year, with approximately 500 enrolled students, to iteratively refine the curriculum components. Data collection instruments include a 40-item AI Competency Inventory, course-embedded assessments, student performance analytics, and instructor reflection journals. Validity and reliability are established through content validity indices, Cronbach’s alpha (? ? 0.80), and confirmatory factor analysis. Quantitative data are analyzed using structural equation modeling (SEM) to test relationships between competencies, learning activities, and student outcomes, while qualitative data from expert panels and teacher journals are analyzed using thematic analysis with coding triangulation. The study anticipates several key findings. First, a validated set of AI competencies tailored to business contexts will emerge, spanning data literacy, algorithmic thinking, AI ethics and governance, human–AI collaboration, and domain-specific applications. Second, the framework will demonstrate strong predictive validity for student readiness, with SEM results indicating substantial direct effects of experiential learning and project-based assessment on perceived competence and employability. Third, the pilot implementations are expected to yield improved learning gains, higher student engagement, and enhanced alignment with industry needs, evidenced by significant gains in post-course assessments (effect size d > 0.50) and positive instructor feedback. Fourth, facilitators and barriers to adoption—such as faculty preparedness, resource constraints, and institutional policies—will be identified, with actionable strategies proposed to mitigate challenges. The study contributes to knowledge by integrating CBE with AI literacy into business education, offering a theoretically grounded yet pragmatically adaptable framework that connects core competencies, pedagogical approaches, assessment practices, and governance structures to industry demands. It advances the literature on AI-enabled curriculum design, bridging gaps between educational theory and professional practice, and provides a scalable implementation blueprint for higher education institutions seeking to transform business programs. The main conclusion is that a competency-based, AI-literate curriculum can be effectively operationalized through phased development, stakeholder collaboration, and iterative validation, yielding graduates better prepared for AI-driven decision-making. Recommendations include policy reforms to support faculty development, investment in AI-enabled learning environments, ongoing industry–academe partnerships, and the integration of standardized assessment rubrics aligned with professional AI competencies to sustain curricular transformation beyond pilot initiatives.