Construction and application of a multi-source data-driven intelligent model for industry-research demand analysis in smart education
DOI:
https://doi.org/10.5281/zenodo.16953649%20Keywords:
smart education, multi-source data fusion, industry–research demand analysis model, talent cultivation, educational decision support system, AI educationAbstract
In the context of the deepening development of smart education, resolving the structural misalignment between talent cultivation and industry demands has emerged as one of the core challenges in higher education reform. This study proposes a three-phase progressive framework, namely “Data Acquisition–Demand Modeling–Decision Output” to construct the multi-source data-driven intelligent analysis model for industry–research demand, integrating tripartite data from industrial, academic, and policy domains to drive the paradigm shift in educational decision-making from experience-based to data-driven approaches: 1) extracting industry demand profiles are extracted through topic modeling of unstructured recruitment texts to reveal composite competency frameworks; 2) identifying and tracking academic research hotspots and trends through bibliometric and keyword co-occurrence analysis; 3) dynamically calibrating model weights via policy document analysis based on strategic orientations. Applied to the artificial intelligence discipline as an empirical field, the model reveals three domain-specific characteristics: 1) industry demands demonstrate a trinity integration of technical proficiency, industrial applicability, and ethical awareness; 2) academic research undergoes an evolution from technological breakthroughs to scenario-based closed-loop construction, and further to socio-ethical value reconstruction; 3) policy priorities emphasize technological sovereignty and vertical scenario development. Then the model generates hierarchical competency matrices and dynamic-priority knowledge inventories to inform curriculum optimization, accompanied by four evidence-based talent cultivation strategies: 1) establishing a tripartite-integrated educational ecosystem; 2) strengthening industry–academia–research collaborative mechanisms; 3) creating adaptive knowledge renewal and ethical governance frameworks; 4) enhancing interdisciplinary scenario-based innovation capabilities. This study further expands the model’s application scenarios, demonstrating its substantial potential for empowering smart education ecosystems, and outlines future research directions.
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Copyright (c) 2025 Chen Chen, Nan Li, Zhao Zhang, Fang Deng, Maobin Lv, Lili Fan, Wei Dong
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