全民人工智能扫盲:可调整的跨学科社会技术课程

Sri Yash Tadimalla, Mary Lou Maher
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引用次数: 0

摘要

本文介绍了 "全民人工智能扫盲 "课程,旨在促进各级教育对人工智能、其社会技术影响及其实际应用的跨学科理解。随着人工智能(AI)的快速发展,对人工智能素养的需求已经超越了传统的人工智能教育课程。人工智能素养的概念有很多种,包括公共素养、设计师的能力培养、对人工智能概念的理解以及特定领域的技能提升。这些概念大多是在 ChatGPT 等生成式人工智能(Gen-AI)工具公开发布之前确立的。人工智能教育通过技术视角关注人工智能的原理和应用,强调掌握人工智能原理、这些技术背后的数学基础以及实施人工智能解决方案所需的编程和数学技能。在 "全民人工智能扫盲 "中,我们强调均衡的课程设置,包括技术和非技术学习目标,以便在跨学科的社会技术背景下对人工智能技术进行概念理解和批判性评估。本文介绍了人工智能素养的四大支柱:了解人工智能的范围和技术层面、学习如何以知情和负责任的方式与人工智能进行互动、伦理和负责任的人工智能的社会技术问题以及人工智能的社会和未来影响。将人工智能教育的所有学习成果纳入计算机科学专业固然重要,但也可以针对其他学习环境调整学习成果,包括非计算机科学专业、高中夏令营、成人劳动力和公众。本文倡导人工智能扫盲教育的转变,提供一种更加跨学科的社会技术方法,作为扩大人工智能参与的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AI Literacy for All: Adjustable Interdisciplinary Socio-technical Curriculum
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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