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Stephen P. Kaputsos

dblp:249/3252 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › AI education
K-12 AI education
0.512021
Teacher Perspectives on How To Train Your Robot: A Middle School AI and Ethics Curriculum · AAAI 2021
Computing education
teacher professional development
0.112021
Teacher Perspectives on How To Train Your Robot: A Middle School AI and Ethics Curriculum · AAAI 2021

Methods — techniques the papers use, named apart from their topics

teacher interviews · 0.5
YearPublicationVenuePosition
2021 Teacher Perspectives on How To Train Your Robot: A Middle School AI and Ethics Curriculum
abstract
To enable a diverse citizenry to fully participate in future society, we must prepare all students to construct and critique emerging technologies like Artificial Intelligence (AI). Classrooms are important spaces to teach students these skills, however there are few AI curricula that have been developed for and used by K-12 teachers. We developed the \textit{How to Train Your Robot: AI and Ethics Curriculum} for middle school teachers who want to introduce AI to their students. This paper describes the curriculum and professional development we used to prepare teachers to run a five-day AI course. Before and after they ran the curriculum, we interviewed teachers to understand their opinions on pedagogical approaches to teaching AI, meeting students' needs, and the feasibility of doing the activities in the classroom. Our results indicate that, with appropriate training, even teachers who were new to computer science felt prepared and successfully engaged their students in the topic. We hope our insights will inform future efforts to realize AI education in primary and secondary classrooms.
Randi Williams, Stephen P. Kaputsos, Cynthia Breazeal
AAAI2