Rapid advances in new-energy vehicles, intelligent connectivity, software-defined vehicles, and data-driven engineering are reshaping competency requirements in the automotive industry. This study uses a sequential mixed-methods design to identify a competency framework and assess perceived industry–education alignment. The qualitative phase draws on 34 semi-structured interviews with 10 enterprise professionals, 10 university faculty members, and 14 students. Interview records were anonymized and condensed during data preparation; semantically similar responses were summarized rather than treated as verbatim transcripts. The qualitative evidence and the actual survey instrument yielded nine competencies organized into three interrelated domains: industry insight, intelligent innovation, and ecosystem collaboration. The quantitative phase analyzed 197 valid questionnaires (72 enterprise professionals, 61 faculty members, and 64 students). Enterprise ratings of competency importance are used as a perceptual proxy for industry demand, whereas faculty and student ratings of current university cultivation are used as a perceptual proxy for educational provision. Welch tests, 95% confidence intervals, Hedges' g, Holm adjustment, importance–performance analysis (IPA), and 3,000 stratified bootstrap replications are employed. All nine perceived demand–provision gaps are statistically significant. The largest gaps concern university–industry collaborative practice (Δ=1.216), data-driven innovation (Δ=1.158), and intelligent tool application (Δ=1.065). These three competencies are also located in the high-importance/low-provision quadrant, with bootstrap priority probabilities of 99.7%, 100.0%, and 96.9%, respectively. Parallel analysis indicates one dominant common factor and the candidate three factors are highly correlated, so the three-domain structure is interpreted as a theoretically and qualitatively grounded content framework rather than three fully distinct latent constructs. The mismatch is structural rather than generalized: provision is relatively stronger in industry cognition but weaker in data-driven, technology-enabled, and authentic collaborative practice.

Huang Rong, Liao Shuangshuang, Mao Shuai. Competency Reconstruction and Industry–Education Alignment for Innovation and Entrepreneurship Talent in the Automotive Industry. Frontiers in Educational Research (2026), Vol. 9, Issue 10: 8-15. https://doi.org/10.25236/FER.2026.091002.

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