International Cooperation Models for Economics and Management Talent Development in the AI Era: Evidence from Beijing Applied Universities

Abstract

The rapid advancement of artificial intelligence poses unprecedented challenges for international cooperation in developing economics and management talent. This study employs a mixed-methods design—combining interviews, questionnaires, and policy document analysis—to identify core competency demands and key elements of innovative international cooperation models. The analysis yields four principal findings. First, knowledge structure, practical competencies, and international perspective constitute the core demand dimensions, with AI literacy penetration—defined as the diffusion of AI-related knowledge, skills, and competencies among individuals—and cross-cultural empathy emerging as potential differentiators between applied and research-oriented universities. This differentiation, however, is based on respondents’ subjective comparative judgments and awaits validation through direct institutional sampling. Second, AI literacy penetration exerts the strongest effect on cooperation effectiveness, with the three competencies accounting for the majority of explained variance. Third, a competency-to-resources-to-institutions-to-effectiveness transmission chain is detected, with indirect effects accounting for nearly half of the total effect. Fourth, the competency-origin model demonstrates superior fit over resource-origin alternatives. Policy document analysis shows AI-related keywords growing at roughly four to five times the rate of international cooperation keywords over the past decade, corroborating the policy urgency of this research. This study challenges the implicit resource-dominant logic in international cooperation research and offers empirical evidence to inform strategic adjustments in municipal applied universities.