Research on the Three-Tier Competency Development Path for Field Engineers Empowered by Generative Artificial Intelligence
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Abstract
Field engineers in manufacturing, infrastructure and energy industries serve as core professionals linking technical solutions to on-site implementation. Stationed at project sites long-term, they undertake comprehensive work including equipment commissioning, emergency troubleshooting, process optimization, safety supervision and customer communication, with ever-upgrading competency requirements. Featuring diverse functions such as text generation, simulation deduction, case reproduction, real-time Q&A and data review, generative artificial intelligence (GenAI) aligns with the phased growth pattern of field engineers, delivering a low-cost, highly adaptable and iterable digital training carrier. Based on the growth stages of field engineers, this paper constructs a three-tier competency framework consisting of Basic Operation Tier, Comprehensive Handling Tier and Innovative Problem-Solving Tier. Combined with application scenarios of GenAI tools, it sorts out the internal logic of AI-enabled competency cultivation at each tier, designs supporting implementation paths, and develops five sets of quantitative analysis tables to establish systems for status research, competency indicators, tool adaptation, implementation procedures and effect evaluation. Targeting enterprise training centers, engineering majors in vocational colleges and industrial skill training institutions, this research provides actionable digital talent training schemes, fills resource gaps in traditional offline training modes, and accelerates job adaptation while improving comprehensive professional capabilities of field engineers.
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