🤖 AI Summary
This work addresses the inadequacy of traditional engineering education in meeting the urgent demand for a new generation of engineers equipped for the era of generative and autonomous intelligence. It proposes the ACCEL educational framework, which introduces a novel competency model for “autonomous intelligence engineers.” The framework systematically cultivates core capabilities—including intent articulation, multi-agent orchestration, output evaluation, and ethical judgment—through three integrated pathways: curriculum restructuring, collaborative mechanisms, and lifelong learning. Grounded in principal–agent theory, research on trust in automation, and international AI competency standards, ACCEL incorporates a commission–verification instructional cycle, governance-oriented ethics education, and an innovative assessment system. It identifies critical risks such as automation bias and skill atrophy, and advances engineering education beyond incremental reform by shifting its focus from artifact-centric outcomes to cultivating judgment over autonomous sociotechnical systems.
📝 Abstract
Generative and agentic artificial intelligence (AI) are reconfiguring software and systems engineering from a discipline centered on human authorship of artifacts to one focused on directing, verifying, and governing autonomous systems. This transition demands a new professional archetype, the \emph{agentic engineer}, whose enduring value lies in intent specification, orchestration of multi-agent workflows, critical evaluation of machine-generated outputs, and ethical judgment. This article presents an integrative conceptual synthesis across engineering education, computing education, human--AI interaction, human factors, and the learning sciences to derive an evidence-grounded educational architecture for this archetype. We introduce the ACCEL framework (Agentic Competencies through Curricula, Collaboration, and Enduring Learning), which organizes five competency pillars and maps them to three delivery vectors: curricula, collaboration, and continuous learning. Drawing on agency theory, trust-in-automation research, and empirical studies of AI-assisted programming, including evidence that AI benefits are unevenly realized and often misperceived, we propose a scaffolded curriculum, a delegation--verification pedagogical loop for human--AI teaming, redesigned assessment, governance-literate ethics integration, and alignment with current curricular guidelines and international AI competency frameworks. We identify key risks, including automation bias, deskilling, superficial engagement, and diffuse accountability, and conclude that educating the agentic engineer requires systemic transformation rather than incremental curricular change: instruction must shift from producing artifacts to exercising judgment over increasingly autonomous socio-technical systems.