This empirical research full paper study investigates how senior mechanical engineering students perceive Generative AI (GenAI) and which professional competencies they view as most critical for success in an AI-augmented engineering landscape. Using a cross-sectional survey of 80 senior mechanical engineering students at a large R1 university, we examine: (1) students’ perceptions, usage patterns, and concerns regarding GenAI; (2) the perceived importance hierarchy among 36 engineering Knowledge, Skills, and Abilities (KSAs); and (3) whether internship experience is associated with differences in KSA valuation. Grounded in the JO-HAI (Joint Optimization of Human-skill and Artificial Intelligence) Framework, a tripartite synthesis integrating Socio-Technical Systems (STS) theory, the hidden curriculum concept, and a proposed Dynamic Competency Interaction Model (DCIM), results show that students prioritize human-centric competencies (communication, M = 4.79; critical thinking, M = 4.77; teamwork, M = 4.70; adaptability, M = 4.56) over specialized technical knowledge. A pronounced awareness–adoption gap characterizes GenAI engagement (89.5% familiar; 4.7% daily use), with accuracy (66.3%), job displacement (65.1%), and ethics (60.5%) as leading concerns. Exploratory analyses at the uncorrected p < 0.05 level suggest internship experience may modestly shift competency valuations (interns rating flexibility higher, p = 0.036; non-interns rating applied technical knowledge higher, p = 0.027); however, these effects do not survive Bonferroni correction and should be interpreted as hypothesis-generating. The JO-HAI Framework offers an integrative lens for understanding competency development in AI-augmented professions, with implications for curriculum design, AI literacy, responsible AI use, and experiential learning.
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