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Galit Lukin

dblp:239/8408 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1Applied, interdisciplinary, general and emerging computing · 1

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.

Human-computer interaction and pervasive computing
1 paper
Learning and educational technologies · 77% Human-AI interaction · 23%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › AI education
AI literacy
0.412020
Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts · AAAI 2020
Learning and educational technologies › AI in education
conversational agents for learning
0.412020
Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts · AAAI 2020
YearPublicationVenuePosition
2020 Zhorai: Designing a Conversational Agent for Children to Explore Machine Learning Concepts
abstract
Understanding how machines learn is critical for children to develop useful mental models for exploring artificial intelligence (AI) and smart devices that they now frequently interact with. Although children are very familiar with having conversations with conversational agents like Siri and Alexa, children often have limited knowledge about AI and machine learning. We leverage their existing familiarity and present Zhorai, a conversational platform and curriculum designed to help young children understand how machines learn. Children ages eight to eleven train an agent through conversation and understand how the knowledge is represented using visualizations. This paper describes how we designed the curriculum and evaluated its effectiveness with 14 children in small groups. We found that the conversational aspect of the platform increased engagement during learning and the novel visualizations helped make machine knowledge understandable. As a result, we make recommendations for future iterations of Zhorai and approaches for teaching AI to children.
Phoebe Lin, Jessica Van Brummelen, Galit Lukin, Randi Williams, Cynthia Breazeal
AAAI3
2019 Staying in the Zone: Sequencing Content in Classrooms Based on the Zone of Proximal Development
Oded Vainas, Yossi Ben David, Ran Gilad-Bachrach, Meitar Ronen, Ori Bar-Ilan, Roi Shillo, Galit Lukin, Daniel Sitton
EDM7