Beyond Deterministic Design: Engineering Education for AI-Enabled Heterogeneous Systems
TL;DR
This constructive position paper proposes the “vibe-designer”—a new professional paradigm that strategically compresses the traditional middle phase of the engineering curriculum to focus on high-level specification, adversarial evaluation, and systemic contextualization.
Abstract
As generative artificial intelligence (AI) automates engineering design, a fundamental reconceptualization of engineering education becomes necessary. This constructive position paper proposes the “vibe-designer”—a new professional paradigm that strategically compresses the traditional middle phase of the engineering curriculum to focus on high-level specification, adversarial evaluation, and systemic contextualization. Drawing on Floridi’s philosophy of information and Simondon’s theory of technical individuation, we argue that the future of engineering lies in the orchestration of conceptual frameworks, ethical stewardship of artificial agents, and “generative judgment”—the capacity to evaluate and contextualize machine-generated solutions within complex sociotechnical systems. We situate this paradigm explicitly within the context of next-generation heterogeneous engineered systems—including cognitive-cyber-physical-social-human (CCPSH) systems, AI-enabled agentic systems-of-systems, and multi-constituent platforms integrating hardware, software, cyberware, and brainware—that constitute the primary site of contemporary systems engineering practice. Central to our argument is the transformation from individual to collaborative creation: creativity in the AI era emerges from dialogical interaction between human intentionality and machine capability. We present a comprehensive curricular architecture based on a transdisciplinary approach, explore epistemic implications, and address critical challenges including the verification gap, liability frameworks, and the preservation of embodied technical intuition. To concretely illustrate the proposed approach, the authors present a hypothetical case study grounded in realistic engineering constraints and current AI capabilities. As a constructive position paper, it aims to provide a foundation for pilot programs and systematic empirical investigation—and to open a space for argument about where engineering education must go next.
