The Future of Education

As artificial intelligence reshapes professional practice, business education faces a curricular design challenge: how to embed AI capability development without reducing it to tool training or isolating it within a single course. This paper presents a theoretically grounded, qualitative curriculum design study that advances a Tiered Curriculum Integration Approach to embed eight workforce-relevant AI competencies across a Bachelor of Business Administration (BBA) program in a US-based university. The study positions AI integration not as technological adoption, but as a problem of curriculum coherence, cognitive development, and the urgent need to build AI competency in business education in response to the evolving business needs. Drawing on curriculum theory, adult learning theory, and constructivist pedagogy, the framework reconceptualizes AI competency development as a vertically scaffolded process across the undergraduate experience. The research documents a multi-phase qualitative methodology used to identify and define eight core competencies aligned to business workforce expectations: AI literacy and fundamentals; data privacy literacy; advanced prompt engineering; AI tools and automation; programming AI agents; critical thinking and problem-solving; strategic AI use; and AI communication skills. Competencies were derived through environmental scanning of employer discourse, review of AI literacy frameworks, and iterative faculty working sessions using thematic analysis. The conceptual contribution of the study lies in the development of a four-tier integration model, namely Foundations, Creation and Control, Building and Evaluation, and Leadership and Impact, explicitly mapped to Bloom’s Taxonomy and the principles of constructive alignment. Rather than adding AI content horizontally, the model vertically scaffolds cognitive complexity from foundational comprehension to strategic evaluation and ethical leadership. This structure reflects constructivist assumptions that learners build knowledge through progressively complex engagement and aligns with adult-learning principles that emphasize relevance, autonomy, and authentic problem contexts. In the next phase of the project, curriculum mapping workshops engaged faculty in aligning course learning outcomes, assessment practices, and AI competencies within existing disciplinary structures. This process operationalized alignment between institutional goals, professional standards, and course-level pedagogy without requiring wholesale curriculum redesign. As a result, this study contributes a replicable model for university programs seeking to systematically and ethically integrate AI into curricular design.