零基础可学的AI课，教学生从理解到动手造AI系统
From Understanding to Creation: A Prerequisite-Free AI Literacy Course with Technical Depth Across Majors
- 用五步循环机制递进教学：定义问题→数据→选模型→评估→反思
- 学生从直觉描述到能设计带安全机制的AI方案，达到创造层级
- 适合各专业学生，尤其适合想学真实AI应用的非技术背景者
多数面向非技术本科生的AI通识课重概念轻技术。本文介绍乔治梅森大学的UNIV 182课程，一门无先修要求的跨专业课程，帮助学生掌握理解、使用、评估和构建AI系统的能力。课程围绕五个机制展开：（1）贯穿始终的统一概念流程（问题定义、数据、模型选择、评估、反思），逐级深化；（2）技术学习与伦理思考同步推进；（3）设置结构化课堂实训（AI Studios），包含文档规范与实时反馈；（4）累积式评估档案袋，每项作业为下一项打基础，最终完成合作式聊天机器人推理实验及团队开发的AI作品展示，接受外部评审；（5）定制AI助教提供课后结构化强化支持。通过四阶段学生作品编码分析，显示学生从描述性直觉推理发展为技术驱动的设计并融入安全机制，达到布卢姆修订版分类法中的“创造”层级。文章还梳理了该设计在各类跨学科AI课程中的定位，并提出可拆分机制与实施建议，说明该课程可适配从通识到学科嵌入的不同场景。课程资料已公开，证明技术深度与广泛可及性可通过支持性教学设计共存。
原文摘要 · Abstract (English)
Most AI literacy courses for non-technical undergraduates emphasize conceptual breadth over technical depth. This paper describes UNIV 182, a prerequisite-free course at George Mason University that teaches undergraduates across majors to understand, use, evaluate, and build AI systems. The course is organized around five mechanisms: (1) a unifying conceptual pipeline (problem definition, data, model selection, evaluation, reflection) traversed repeatedly at increasing sophistication; (2) concurrent integration of ethical reasoning with the technical progression; (3) AI Studios, structured in-class work sessions with documentation protocols and real-time critique; (4) a cumulative assessment portfolio in which each assignment builds competencies required by the next, culminating in a co-authored field experiment on chatbot reasoning and a final project in which teams build AI-enabled artifacts and defend them before external evaluators; and (5) a custom AI agent providing structured reinforcement outside class. The paper situates this design within a comparative taxonomy of cross-major AI literacy courses and pedagogical traditions. Instructor-coded analysis of student artifacts at four assessment stages documents a progression from descriptive, intuition-based reasoning to technically grounded design with integrated safeguards, reaching the Create level of Bloom's revised taxonomy. To support adoption, the paper identifies which mechanisms are separable, which require institutional infrastructure, and how the design adapts to settings ranging from general AI literacy to discipline-embedded offerings. The course is offered as a documented resource, demonstrating that technical depth and broad accessibility can coexist when scaffolding supports both.
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