Educating the Agentic Engineer: Curricula, Collaboration, and Continuous Learning in the AI Era
2026-07-31 • Software Engineering
Software Engineering
AI summaryⓘ
The authors explain that AI is changing engineering from making things by hand to managing smart systems that work on their own. They suggest there needs to be a new type of engineer called an agentic engineer who focuses on guiding AI, checking its work, and making ethical decisions. To train these engineers, the authors propose the ACCEL framework, which builds skills through classes, teamwork, and ongoing learning. They also highlight challenges like relying too much on AI or losing skills and stress that engineering education must change deeply to keep up with these new roles.
agentic engineergenerative AIautonomous systemscurriculum designhuman-AI interactionautomation biasethical judgmentsocio-technical systemsAI-assisted programming
Authors
Mamdouh Alenezi
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.