Reconstructing Higher Education in the Big Data and AI Era: Interdisciplinary Integration and Problem-Driven Talent Cultivation

Abstract

In the context of the transformation of social production methods driven by big data and artificial intelligence (AI), the contradiction between the traditional "discipline-based" model of higher education and the AI-driven demand for "composite competencies" has become increasingly prominent. The outdated talent cultivation and evaluation models fail to meet the demands of the times. This paper, starting from the educational transformation driven by technology, and combining the theory and practice of interdisciplinary integration, proposes a framework for the reconstruction of engineering education centered on "building interdisciplinary knowledge networks — training problem-solving skills — human-machine collaborative innovation practices." It suggests that higher education should transform its talent cultivation model from a "discipline-based" to a "competency-based" approach through the reconstruction of curricula, innovation in teaching models, integration of resource ecosystems, and optimization of talent evaluation systems. The goal is to cultivate interdisciplinary talents with critical thinking, interdisciplinary literacy, human-machine collaboration, and lifelong learning abilities.

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