发表机构
Utah Valley University（犹他谷大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出AI时代劳动力准备的能力阶梯课程现代化框架，将AI增强工作按操作自主性分为五级，映射到课程更新等，主张围绕持久能力针对性现代化而非全面替换。
AI 中文摘要
人工智能正在以远超课程与培训通常适应速度的方式改变计算工作的任务构成。本文是一篇课程框架论文，基于对劳动力市场和软件工程证据的结构化叙事综述，并通过一门探索性试点课程进行说明；该综述为框架提供了支撑，而试点课程仅用于说明，不作为主要证据。核心观点是近期的变革是任务再分配而非完全替代：常规实施正日益自动化，而验证、系统思维、安全以及监督和协调AI（保持人在回路中）的能力价值不断提升。我们将应对措施组织为以能力保障框架，该框架锚定能力阶梯：这是一个五级进阶（触发、自动化、工作流、智能体、智能体团队），用于对AI增强工作的操作自主性及其所需的人工监督进行分类。我们将该阶梯映射到课程层面更新、工作负载感知评估以及可堆叠的劳动力资质，并通过一门面向计算机和商科学生的基于团队的无代码课程的两学期试点进行说明。我们主张围绕持久能力进行针对性现代化，而非全面替换课程，且明确说明证据局限性：劳动力信号受非AI因素混淆，行业报告具有方向性，且试点具有探索性。
英文摘要
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.
