构建跨学科的AI素养课程,让各层次人群理解AI技术与社会影响。
AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum
- 设计四支柱课程:技术理解、生成式AI使用、伦理责任、社会影响。
- 强调非技术学习目标,适配不同教育阶段与人群。
- 推动从纯技术教育转向融合社会技术视角的AI通识教育。
本文提出一项名为「AI Literacy for All」的跨学科课程,旨在促进各教育层级对人工智能的技术、社会技术影响及实际应用的综合理解。随着生成式AI(Gen-AI)工具如ChatGPT的普及,传统以技术为核心的AI教育已显不足。当前的AI素养概念涵盖公众认知、设计者能力、概念理解与领域技能提升,但大多基于生成式AI出现前的框架。本课程强调技术与非技术学习目标并重,涵盖四大支柱:理解AI的技术范畴与边界、以知情且负责任的方式使用生成式AI、探讨伦理与负责任的AI实践,以及分析AI的社会与未来影响。该课程可灵活调整,适用于计算机科学专业、非计算机专业学生、高中暑期营、成人职场及公众群体。论文倡导将AI素养教育转向更跨学科的社会技术路径,以扩大公众参与度。
原文摘要 · Abstract (English)
This paper presents a curriculum, "AI Literacy for All," to promote an interdisciplinary understanding of AI, its socio-technical implications, and its practical applications for all levels of education. With the rapid evolution of artificial intelligence (AI), there is a need for AI literacy that goes beyond the traditional AI education curriculum. AI literacy has been conceptualized in various ways, including public literacy, competency building for designers, conceptual understanding of AI concepts, and domain-specific upskilling. Most of these conceptualizations were established before the public release of Generative AI (Gen-AI) tools like ChatGPT. AI education has focused on the principles and applications of AI through a technical lens that emphasizes the mastery of AI principles, the mathematical foundations underlying these technologies, and the programming and mathematical skills necessary to implement AI solutions. In AI Literacy for All, we emphasize a balanced curriculum that includes technical and non-technical learning outcomes to enable a conceptual understanding and critical evaluation of AI technologies in an interdisciplinary socio-technical context. The paper presents four pillars of AI literacy: understanding the scope and technical dimensions of AI, learning how to interact with Gen-AI in an informed and responsible way, the socio-technical issues of ethical and responsible AI, and the social and future implications of AI. While it is important to include all learning outcomes for AI education in a Computer Science major, the learning outcomes can be adjusted for other learning contexts, including, non-CS majors, high school summer camps, the adult workforce, and the public. This paper advocates for a shift in AI literacy education to offer a more interdisciplinary socio-technical approach as a pathway to broaden participation in AI.
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