The Impact of Generative AI Adoption on Faculty Job Performance in Private Universities in Shandong Province: A Technology Acceptance Model Perspective

ABSTRACT: Generative AI (GenAI) reshapes teaching, research and administration in higher education. Private universities in Shandong feature constrained budgets, heavy teaching loads and urgent digital transformation demands, forming a unique research context. Based on the Technology Acceptance Model (TAM), this study explores how perceived usefulness and ease of use drive faculty’s GenAI adoption intention, and further analyzes AI’s multidimensional effects on their teaching, research and administrative performance. A context-specific framework is constructed through literature review. Results show rational GenAI application effectively eases institutional resource shortages and improves overall faculty efficiency. However, academic integrity risks, data privacy threats, algorithm bias and uneven digital literacy hinder sustainable AI adoption. This paper proposes tiered training, standardized ethical rules and optimized technical support for local private universities, offering references for similar private higher education institutions worldwide.

KEYWORDS : Generative Artificial Intelligence, Private University, Faculty Job Performance, Technology Acceptance Model, Educational Digital Transformation, Shandong Higher Education