Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review
Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review
复制标题
2019年至2022年K-12中的人工智能教学：系统文献综述
DOI：
10.1016/j.caeai.2023.100145
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发表时间：
2023
影响因子：
14.4
通讯作者：
Rizvi S
中科院分区：
文献类型：
--
作者：
Rizvi S
在K-12环境中，人工智能（AI）教学和学习的兴趣正在兴起。虽然一些工作已经探索了用于此目的的教育内容和资源，但关于此类人工智能教育干预措施的有效性的经验证据有限。本文中提出的系统性文献综述的主要目标是研究2019年至2022年期间K-12教学和学习AI的经验证据报告学习成果。通过严格的筛选过程，从使用特定检索词从五个研究数据库中识别的8，175篇论文中，共有28项研究被纳入最终分析。内容分析方法用于合成数据。本文概述了学习者的背景下，教学方法的实证支持的程度，并在研究中包含的AI主题的理论覆盖面的重点。大多数研究报告了认知和情感学习成果的改善。本文最后强调了未来需要进一步研究的关键领域以及与之相关的挑战。虽然研究结果是基于有限的实证研究，但他们认为，一种更加以学习者为中心的方法、情境感知的教学实践以及衡量人工智能学习成果的一致结构，可能有利于K-12学校的人工智能教学。需要进一步研究，以这些见解为基础。
There is an emerging interest in Artificial Intelligence (AI) teaching and learning in the K-12 setting. While some work has explored the educational content and resources used for this purpose, there is limited empirical evidence on the effectiveness of such AI education interventions. The primary objective of the systematic literature review presented in this paper was to examine research with empirical evidence reporting learning outcomes for teaching and learning AI in K-12 between 2019 and 2022. Through a rigorous selection process, a total of 28 studies were included in the final analysis out of 8,175 papers identified from five research databases using specific search terms. A content analysis method was used to synthesise the data. This paper outlines the focus on learners' context, the extent of empirical support for the pedagogical approaches, and the theoretical coverage of AI topics included in the studies. The majority of studies reported an improvement in both cognitive and affective learning outcomes. The paper concludes by highlighting key areas where additional research is needed in the future as well as the challenges associated with them. Although the findings are based on limited empirical studies, they suggest that a more learner-centred approach, context-aware pedagogical practices, and consistent constructs to measure AI learning outcomes could benefit teaching and learning AI in K-12 schools. Further research is needed to build on these insights.
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DOI：
--
发表时间：
2019
期刊：
Technical Symposium on Computer Science Education
影响因子：
--
作者：
Caelin Bryant;Yesheng Chen;Zhen Chen;Jonathan Gilmour;Shyamala Gumidyala;Beatriz Herce;Annabella Koures;Seoyeon Lee;James Msekela;Anh Thu Pham;Halle Remash;Marli Remash;Nolan Schoenle;J. Zimmerman;S. Albright;Samuel A. Rebelsky
通讯作者：
Samuel A. Rebelsky
DOI：
10.1145/3408877.3432513
发表时间：
2021-03
期刊：
Proceedings of the 52nd ACM Technical Symposium on Computer Science Education
影响因子：
--
作者：
Irene A. Lee;Safinah Ali;Helen Zhang;Daniella DiPaola;C. Breazeal
通讯作者：
Irene A. Lee;Safinah Ali;Helen Zhang;Daniella DiPaola;C. Breazeal
DOI：
--
发表时间：
2021
期刊：
KI - Künstliche Intelligenz
影响因子：
--
作者：
Carmen Fernández;Isidoro Hernán;Alberto Fernández
通讯作者：
Alberto Fernández
通过 LearningML 向 10-16 岁学生教授人工智能的在线干预评估
DOI：
--
发表时间：
2021
期刊：
Technical Symposium on Computer Science Education
影响因子：
--
作者：
J. D. Rodríguez;J. Moreno;Marcos Román;G. Robles
通讯作者：
G. Robles
通过角色扮演游戏向 K-12 教授人工智能，质疑智能概念
DOI：
--
发表时间：
2021
期刊：
KI - Künstliche Intelligenz
影响因子：
--
作者：
Julie Henry;Alyson Hernalesteen;Anne
通讯作者：
Anne