The Landscape of Teaching Resources for AI Education

被引：40

作者：

Druga, Stefania ^{[1
]}

Otero, Nancy ^{[2
]}

Ko, Amy J. ^{[1
]}

机构：

[1] Univ Washington, Informat Sch, Seattle, WA 98195 USA

[2] Kitco, San Francisco, CA USA

来源：

PROCEEDINGS OF THE 27TH ACM CONFERENCE ON INNOVATION AND TECHNOLOGY IN COMPUTER SCIENCE EDUCATION, ITICSE 2022, VOL 1 | 2022年

关键词：

AI education; K12; Teaching Support;

D O I：

10.1145/3502718.3524782

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Artificial Intelligence (AI) educational resources such as training tools, interactive demos, and dedicated curriculum are increasingly popular among educators and learners. While prior work has examined pedagogies for promoting AI literacy, it has yet to examine how well technology resources support these pedagogies. To address this gap, we conducted a systematic analysis of existing online resources for AI education, investigating what learning and teaching affordances these resources have to support AI education. We used the Technological Pedagogical Content Knowledge (TPACK) framework to analyze a final corpus of 50 AI resources. We found that most resources support active learning, have digital or physical dependencies, do not include all the five big ideas defined by AI4K12 guidelines, and do not offer built-in support for assessment or feedback. Teaching guides are hard to find or require technical knowledge. Based on our findings, we propose that future AI curricula move from singular activities and demos to more holistic designs that include support, guidance, and flexibility for how AI technology, concepts, and pedagogy play out in the classroom.

引用

页码：96 / 102

页数：7

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