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Journal of library and information science in agriculture

   

Enlightenment of AI Literacy Educational Designs and Practices at Japanese MDASH Literacy-level Approved Universities

DAI Xinwei, LI Feng   

  1. Taizhou University Library, Taizhou 318000
  • Received:2025-02-17 Online:2025-06-04

Abstract:

[Purpose/Significance] Amid the global wave of digital transformation in education, artificial intelligence (AI) has emerged as a driving force behind Japanese educational reform, propelling the country's education system toward an "AI+" model. The "Approved Program for Mathematics,Data science and AI Smart Higher Education" (MDASH), led by the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), outlines a comprehensive framework for designing and implementing AI literacy (AIL) education in Japanese universities. MDASH not only reflects the Japanese strategic response to the AI-driven future, but also provides valuable theoretical references and practical guidance for enhancing AIL education in China. This study provides a detailed analysis of the "MDASH literacy-level" (MDASHL) curriculum model design, paying a particular attention to the model's modules and the mechanisms of interaction between them. It also examines the theoretical references from MDASHL review system to the AIL framework studies. The study proposes innovative implementation strategies for AIL education from unique perspectives, especially the "industry-academia integration" aspect. [Method/Process] Using internet research and literature analysis, starting with an exploration of Japanese national AI policy landscape, the study traces the evolution of Japanese AI policies and the contextual origins of the MDASH. It describes the objectives and philosophy of Japanese AIL education and delves into the theoretical underpinnings of the MDASHL curriculum model based on the mapping relationship between indicators of AIL frameworks and the components of the MDASHL review system. We select Hokuriku University, Wakayama University, Chiba University, and Kansai Univerisity as samples because they were approved by MDASHL and demonstrated exemplary effects. We introduce their subject curriculum design and specific teaching initiatives, identify the commonalities and unique characteristics of their AIL education, and further elaborate on their specific educational implementation pathways. [Results/Conclusions] The findings indicate that the Japanese MDASHL curriculum model is deeply rooted in the AIL frameworks. It summarizes five educational directions for Japanese AI literacy education: recognition, realization, comprehension, ethics, and practical operation. By comparing the current status of AIL education in China and Japan, the study found that Japanese AIL education has achieved rapid responsiveness and systematic development under the unified coordination of MEXT. It suggests that Japanese AI literacy education strategies have localized value, from which beneficial insights can be drawn in three areas: strategic planning, curriculum design, and industry-academia integration. These strategies provide innovative solutions for developing AIL education systems in Chinese universities. However, this study acknowledges limitations in the sample size. To comprehensively capture the full landscape of Japanese AIL education development, future research should expand the sample size, summarize its patterns and characteristics more thoroughly, and enhance the persuasiveness and generalizability of the findings.

Key words: AI literacy education, educational design, educational practice, higher education, Japan

CLC Number: 

  • G252

Fig.1

Position of AIL education in AI talent development system"

Table 1

Japanese MDASHL curriculum model"

模块 要素 教学方法
引入

1.数据、AI在社会中的运用

1-1.社会上正在发生的变化

1-2.社会上使用的数据

1-3.数据、AI的应用领域

1-4.面向数据、AI运用的技术

1-5.运用数据、AI的场景

1-6.运用数据、AI的最新动向

1.通过视频播放,展示数据赋能或AI赋能的实际案例(例如MOOC等)开展翻转学习;在授课过程中对数据和AI的广泛应用及其技术原理作详细解说

2.学生应以小组形式进行探讨,并分享生活中数据与AI的实际应用案例,避免单向的授课方式

基础

2.数据素养

2-1.读取数据

2-2.解释数据

2-3.处理数据

1.根据高校自身特点设立合适的主题,使用真实数据(或模拟数据)进行授课

2.学生亲身体验数据运用的部分环节,比如动手实现数据可视化

3.如有必要,最好准备课后的补充授课(补讲等)

须知

3.数据、AI运用的注意事项

3-1.操作数据、AI时的注意事项

3-2.数据保护注意事项

1.引导学生将数据驱动型社会的风险视为切身课题

2.关于数据、AI引起的挑战进行小组讨论,不局限于单向授课的形式

选修

4.可选课程

4-1.统计与数学基础

4-2.算法基础

4-3.数据结构与编程基础

4-4.时间序列数据分析

4-5.自然言语处理

4-6.图像识别

4-7.数据处理

4-8.数据活用实践(监督学习)

4-9.数据活用实践(无监督学习)

1.本内容作为可选项,高校根据自身特点选择学习内容

2.高校根据自身特点设定合适的题目,使用真实数据(或模拟数据)进行授课

3.根据学生意愿满足授课需求(高校间合作等)

Fig.2

MDASHL curriculum design framework"

Table 2

Mainstream AIL frameworks at home and abroad"

编码 提出者(年份) 对象 二级编码&AIL框架指标
#1 LONG(2020)[16] 未明确 #1.1 AI概念初识(What is AI)
#1.2 AI能力范畴(What can AI do)
#1.3 AI技术原理(How does AI work)
#1.4 AI伦理应用(How should AI be used)
#1.5 AI公众认知(How do people perceive AI)
#2 NG(2021)[17] K-16、公众 #2.1 AI认知与理解(Know and understand AI)
#2.2 AI使用与应用(Use and apply AI)
#2.3 AI评估与创造(Evaluate and create AI)
#2.4 AI伦理(AI ethics)
#3 CHEE(2024)[18] K-16、教师、组织、职场人群、公众 #3.1 AI设备与软件操作能力(AI device and software)
#3.2 数据与算法素养(Data and algorithm literacy)
#3.3 问题解决能力(Problem solving)
#3.4 交互协作能力(Communication and collaboration)
#3.5 AI伦理能力(AI ethics)
#3.6 职业相关能力(Career-related competences)
#3.7 AI内容创作能力(AI content creation)
#3.8 情感能力(Affective competences)
#4 UNESCO(2024)[20] 学生 #4.1 以人为本的AI思维方式(Human-centred Mindset)
#4.2 AI伦理(Ethics of AI)
#4.3 AI技术与应用(AI techniques and applications)
#4.4 AI系统设计(AI system design)
#5 张银荣(2022)[21] 中国学生 #5.1 AI知识(对应:文化基础)
#5.2 AI能力(对应:自主发展)
#5.3 AI伦理(对应:社会参与)
#6 杨鸿武(2022)[22] K-12 #6.1 核心概念
#6.2 技术实践
#6.3 跨学科思维
#6.4 伦理态度

Table 3

Japanese AIL education directions embodied in its AIL education framework"

项目序号 项目内容 MDASHL课程模型要素 AIL框架指标 AIL教育方向
1 AI对当前正在进行的社会变革(第四次产业革命、Society5.0、数据驱动型社会等)有着深刻的贡献,并且与我们的生活有着密切的联系

引入:

1-1. 社会上正在发生的变化

1-6.运用AI的最新动向

#1.1、#2.1、#3.8、#4.3、#5.1、#6.1 熟识AI概念、把握AI动态
2 AI所针对的“社会中被活用的数据”和“数据的活用领域”非常广泛,可以成为解决日常生活和社会课题的有用工具

引入:

1-3.AI的应用领域

#1.2、#2.1、#3.8、#4.3、#5.1、#6.1 加强AI意识、领悟AI意蕴
3 展示了各种数据利用现场的数据利用事例,AI通过与各种应用领域(流通、制造、金融、服务、公共基础设施、医疗保健等)的知识相结合来创造价值

引入:

1-4.面向AI运用的技术

1-5.运用AI的场景

#1.2、#1.3、#2.1、#2.3、#3.6、#4.3、#5.1、#6.1、#6.3 了解AI原理、拓展AI应用
4 AI并不是万能的,重要的是要考虑其利用时的各种注意事项(ELSI、个人信息、数据伦理、AI社会原则等)

须知:

3-1.操作AI的注意事项

#1.4、#2.3、#2.4、#3.5、#4.1、#4.2、#5.3、#6.3、#6.4 遵守AI准则、承担AI责任
5 借助真实数据(包括学术数据等)和课题演练,结合社会案例的实践教学,学习基础的AI应用方法

基础:

2-1.读取数据

2-2.解释数据

2-3.处理数据

#1.3、#1.5、#2.2、#3.1、#3.2、#3.3、#3.4、#3.7、#4.4、#5.2、#6.2 依托AI实操、培养AI技能

Fig.3

“Mathematics, Data Science, and AI Education Program” at Wakayama University"

Table 4

Comparison of AIL education in four Japanese universities"

高校名称 项目启动年(MDASHL认证年) 项目定位 项目主导者 面向学生范围

MDASHL

课程模块

MDASHL

课程方案类型

学分 必修 颁发DOB 线上教学 产学融合 MDASHL+认证 认证优势
北陆大学 2022(2023) 培育具备AI相关数据素养的专业型人才 全校教务委员会 全校 引入、基础、须知、选修 2 2、3、4

(部分选修)

教学低门槛、趣味驱动
和歌山大学 2020(2021) 面向社会5.0的高端型技术人才培育 数据智能教育研究部门 3 2 教学支持举措完备
千叶大学 2020(2021) “千叶大学国际化人才培养”项目环节其一 情报战略机构数据科学部门 1 3 AI必修课、AI辅修专业并行发展
关西大学 2021(2022) 打造综合性超级大学的校园氛围 教育推进委员会+院长、研究科长会议 1 4 13学院文理融合的课程设计

Fig.4

The digital intelligence industry-academia integration system of AIL education in Chinese universities"

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