Construction of AI Teaching Ethical Norms for Business Faculty in Private Undergraduate Institutions
The integration of generative artificial intelligence (GenAI) into business classrooms has brought efficiency gains, but it has also introduced four types of ethical risks: student data security, algorithmic bias, academic integrity, and pedagogical responsibility. Existing norms are formulated for general scenarios and fail to cover the unique structure of the intersection of student data and corporate data in business classrooms of private undergraduate institutions. This paper reveals the generative logic of these risks from four dimensions: dual data overlap, algorithmic evaluation labeling, homogenization of assignment generation, and dilution of human-machine responsibility. On this basis, it constructs an ethical norm system led by values, supported by four-dimensional pillars, and implemented through execution. It designs distinctive clauses such as corporate data authorization and case desensitization, and builds a closed-loop of warning and evaluation, providing a reference for similar institutions.
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