The expanding use of artificial intelligence (AI) in organizational decision-making creates new requirements for postgraduate management education. For engineering-oriented universities, the challenge is to connect technological expertise with managerial reasoning through a coherent educational framework. This paper examines how AI courses can be embedded across master’s-level management programs, with particular attention to the Chinese higher education context. Using a design-oriented conceptual synthesis of research on AI literacy, constructive alignment, management, and responsible AI, the paper proposes a framework organized around five competencies: managerial problem formulation, data and model reasoning, AI-supported inquiry, organizational implementation, and responsible judgment. These competencies are developed through entry-level diagnosis, foundational courses, discipline-specific modules, integrative projects, and thesis supervision. A constructed supply chain management scenario illustrates how the framework could translate into teaching practice. The paper also proposes an evaluation strategy that distinguishes AI-assisted task performance from independent understanding and delayed transfer. Its principal contribution is a program-level approach that connects learning outcomes, authentic management tasks, individual evidence, and institutional coordination. The framework has not been empirically validated; its educational effectiveness and implementation costs require subsequent investigation. Nevertheless, it provides a practical foundation for developing AI-informed managerial judgment while preserving disciplinary depth and academic independence.

Xinna Zhao, Chenyang Bao, Integrating Artificial Intelligence Courses into Master’s-Level Management Education at Engineering-Oriented Universities: Pathways and Practice. International Journal of Educational Innovation and Science (2026), Vol. 7, Issue 1: 119-128. https://doi.org/10.38007/IJEIS.2026.070115.

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