K-12 AI curricula: A mapping of government-endorsed AI curricula（Ⅲ）

译题  K-12人工智能课程：政府认可的人工智能课程图谱（三）

Product by UNESCO （United Nations Educational， Scientific and Cultural Organization） 联合国教科文组织

第一份关于K-12（学前教育至高中教育的基础教育阶段）人工智能课程全球状况的报告，意在开发有效的支持工具和框架来促进青少年人工智能素养的发展。

Curriculum integration and management

Curricula are integrated into existing education systems through a number of different models：

Discrete AI curricula are developed in an independent subject category within the national or local curriculum framework. These curricula have their own time allocations， textbooks and resources， as in the case of Chinas Foundations of AI under Information Science and Technology for grades 10 to 12.

Embedded AI curricula are developed and contained within other subject categories in the national or local curriculum framework. AI most commonly becomes a topic within ICT or computer science but may alternatively be part of language， mathematics， science or engineering （see the right Figure）. In the Republic of Korea， two elective AI subjects have been developed， one falling within the mathematics subject group and the other in technology and home economics. Curricula can also be designed to be embedded flexibly into any subject depending on teacher capacity and interest. This is the case for the MIT DAILy Curriculum.

Interdisciplinary AI curricula are implemented in systems with particular mandates for cross-subject work and associated time. These curricula target AI learning outcomes through project-based learning involving multiple subject areas. An example is seen in Portugals curriculum frameworks， which feature ‘autonomous curriculum domains， or projects that must engage two or three disciplines in an interdisciplinary approach. In the UAE， AI is integrated into a range of subjects including ICT， science， maths， language， social studies and moral education.

Multiple-modality AI curricula have core requirements which are implemented during school time and supported by traditional resources such as facilitator guides and textbooks， but also leverage informal learning opportunities such as out-of-school resource networks and national or international competitions. An example of such a curriculum is the IBM-CBSE AI Curriculum for Grade XI & XII， which provides a gradual transition from guided to independent learning and links to competitions and industry mentorship.

Flexible AI curricula can be implemented through one or more integration mechanisms at the discretion of regions， school networks or individual schools. Examples include the ATL AI modules curriculum in India， which can be embedded， interdisciplinary or delivered through out-of-school models such as extracurricular activities; and Digital Skills in Saudi Arabia， which can be implemented as either a discrete or embedded curriculum. For some curricula， the embedding mechanisms are at the discretion of regions， schools or networks. These include the French-speaking Belgiums IT Repository （2nd and 3rd degree technical transition）， and Germanys Algorithmen erkennen und formulieren [Identifying and Formulating Algorithms] curriculum.

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课程整合与管理

人工智能课程可以通过多种方式被整合至现有的教育系统：

独立人工智能课程。该类型课程是在国家或地方课程框架内开发的独立学科类别。这些课程有自己的时间分配、教材和资源，比如，中国面向高中1～2年级的信息科技学科中设置的人工智能基础课程。

归入式的人工智能课程。该类课程在国家或地方课程框架下的其他学科类别中开发，并被纳入其中。人工智能不仅是信息通信技术或计算机科学中最常见的主题，也是语言、数学、科学或工程学科的一部分（见右上图）。韩国已经开发了两门人工智能选修课程，其中一门隶属于数学学科群，另一门被纳入技术家庭经济学科群。人工智能课程也可以根据教师的能力和兴趣，灵活地归入任何学科。麻省理工学院的DAILy课程就属于此种情况。

跨学科人工智能课程。此类课程往往在对跨学科工作和时间有特殊要求的系统中实施。这些课程旨在通过涵盖多学科领域的基于项目的学习，实现人工智能的学习结果。以葡萄牙的人工智能课程框架为例，其特点是“自主课程领域”，或项目必须涉及两到三个学科的跨学科方法。在阿联酋，人工智能被整合到包括信息通信技术、科学、数学、语言、社会研究和品德教育等在内的一系列学科当中。

多课程方式相结合的人工智能课程。该类课程的核心要求在于：不仅需要在开学期间实施，且需要传统教学资源的支持（如教师指导和教材），同时也需利用校外资源网络和国家或国际竞赛等非正式的学习机会。此类课程的代表是“IBM-CBSE面向11和12年级的人工智能课程”，该课程结合竞 赛和行业指导，帮助学生实现从引导式学习到独立学习的逐步过渡。

弹性人工智能课程。此类课程可根据地区、学校网络或个别学校的实际情况，通过一个或多个整合机制去实施课程。例如，印度的ATL人工智能模块课程，可通过归入其他课程、跨学科合作或与课外活动结合等校外模式实施；沙特阿拉伯的数字技能课程，可作为独立或归入式的课程实施。

还有部分课程的归入机制取决于地区、学校或网络。这类课程包括比利时法语区的信息技术数据库（二、三级技术过渡），以及德国的识别和表述算法课程。