🤖 AI Summary
This study addresses the misalignment between rapidly evolving skill demands driven by artificial intelligence and the current computing education landscape. It proposes a competency-assured curriculum modernization framework that reconceptualizes AI-induced task transformation not as replacement but as redistribution. The framework introduces a five-tier “competency ladder”—spanning Trigger, Automation, Workflow, AI Agent, and Agent Team—to delineate levels of human autonomy and oversight in human-AI collaboration. Guided by this model, the work outlines strategies for curriculum redesign, assessment development, and stackable credentialing. Feasibility is demonstrated through a structured narrative review and a two-semester interdisciplinary pilot course integrating labor market analytics, empirical software engineering, and no-code collaborative platforms, offering computing and business students a targeted pathway to thrive in AI-augmented workplaces and facilitating a smooth educational transition toward human-AI collaborative paradigms.
📝 Abstract
Artificial intelligence is changing the task composition of computing work faster than curricula and training typically adapt. This is a curriculum-framework paper, grounded in a structured narrative review of labor-market and software-engineering evidence and illustrated through an exploratory pilot course: the review supports the framework, and the pilot illustrates it rather than serving as primary evidence. The central claim is that near-term change is task reallocation rather than full replacement: routine implementation is increasingly automated while verification, systems thinking, security, and the ability to supervise and orchestrate AI (keeping a human in the loop) gain value. We organize the response as a capability-assurance framework anchored by a Capability Ladder: a five-level progression (trigger, automation, workflow, AI agent, agent team) that classifies the operational autonomy of AI-augmented work and the human supervision it requires. We map the ladder to course-level updates, workload-aware assessment, and stackable workforce credentials, and illustrate it through a two-semester pilot of a team-based, no-code course enrolling computing and business students. We argue for targeted modernization around durable capabilities rather than wholesale curriculum replacement, and we are explicit about evidence limits: labor signals are confounded by non-AI forces, industry reports are directional, and the pilot is exploratory.