The rapid advancement of artificial intelligence (AI) has introduced new demands on the cultivation of big data professionals. Traditional laboratories, which often fall short in computational capacity, data resources, and scenario integration, are increasingly inadequate in the AI era. Local universities face the dual challenges of limited resources and disconnection from regional economic development. This paper proposes a laboratory construction model centered on building a general-purpose technical foundation, integrating regionally distinctive data resources, and driving instruction with real-world business scenarios. We design a three-layer cloud-terminal architecture, embed multiple industry-sector desensitized datasets, and develop a four-year progressive project-based experimental system. Taking a university-industry collaboration project at a local university as a practical case, we demonstrate the development process and application outcomes of a big data laboratory platform. Practice shows that this model effectively enhances students’ AI application capabilities and their employment competitiveness in serving regional development, providing a replicable paradigm for big data laboratory construction in local universities.

Xiaodong Tang. Construction and Practice of Big Data Program Laboratory in Local Universities in the AI Era. Frontiers in Educational Research (2026), Vol. 9, Issue 6: 30-34. https://doi.org/10.25236/FER.2026.090604.

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