Research on Practical Dilemmas and Optimization Paths of Industry Education Integration in Private Universities under the AI+EOBE Model

Abstract

In the era of digital economy, artificial intelligence promotes the digital transformation of higher education. Industry‑education integration serves as a vital approach for private universities to cultivate application‑oriented talents and resolve the mismatch between university‑enterprise supply and demand as well as homogenized talent training. Outcome‑Based Education (EOBE) centers on competency output and emphasizes backward design and continuous iteration, which fits the school‑running orientation of private universities to serve regional industries. At present, industry‑education integration in private universities is confronted with problems such as superficial integration, lagging training systems, rigid evaluation mechanisms and insufficient digital empowerment. Existing studies mostly explore intelligent education and outcome‑oriented education separately, while in‑depth research on industry‑education integration that couples AI and EOBE for private universities remains scarce. Adopting literature analysis, questionnaire‑interview survey and case study methods, this paper analyzes practical dilemmas based on the institutional characteristics of private universities, and constructs an iterative closed‑loop optimization system of AI+EOBE. It enriches relevant theories and provides practical references for industry‑education integration in private universities.