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Danny Schmidt

dblp:390/8432 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2026
—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 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › AI education
AI literacy
1.012026
Co-Designing Unplugged Learning Activities with K-2 Teachers for Early AI Literacy Education · AAAI 2026
Computing education › AI education
K-12 AI education
1.012026
Co-Designing Unplugged Learning Activities with K-2 Teachers for Early AI Literacy Education · AAAI 2026
Computing education
teacher professional development
0.312026
Co-Designing Unplugged Learning Activities with K-2 Teachers for Early AI Literacy Education · AAAI 2026

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

unplugged learning activities · 1.0co-design · 1.0
YearPublicationVenuePosition
2026 Co-Designing Unplugged Learning Activities with K-2 Teachers for Early AI Literacy Education
abstract
Introducing AI concepts in the earliest years of schooling can help children make sense of intelligent technologies, yet few resources exist for K–2 classrooms. This paper presents the design and outcomes of a professional development (PD) program supporting K–2 teachers as they explored AI literacy and co-designed unplugged classroom activities. Grounded in AI4K12's Five Big Ideas in AI framework, the PD combined hands-on learning, collaborative design, and micro-teaching opportunities. Guiding activities included Train the AI (pattern recognition), What Happens Next? (consequences of AI use), Who Did the Robot Hear? (data diversity), and Teach the Robot (model training). Educators then created screen-free, English Language Arts-aligned activities using storytelling, sorting, and embodied play to introduce AI topics such as machine learning and fairness in age-appropriate ways. The PD emphasized integrating AI into existing K–2 literacy routines, lowering implementation barriers while supporting vocabulary development, reasoning, and empathy. Teacher reflections revealed growing confidence in adapting AI topics for young learners and highlighted the value of peer collaboration, clear language, and tactile materials.
Jessica Vandenberg, Keisha Bailey, Claire Aguiar, Cecilia Xuning Zhang, Danny Schmidt, Treshonda Rutledge, Bradford W. Mott, Joseph P. Wilson
AAAI5