Building early AI literacy: developing a pedagogical framework for early childhood education

This study addresses a pressing need in early childhood education: a comprehensive pedagogical framework for early AI learning. Educators currently lack an instructionally actionable framework that specifies both what to teach and how to teach it for preliterate learners. Grounded in constructivist, constructionist, sociocultural, and embodied learning theories, the study proposes a 3C-3H + DS framework—cognitively appropriate, culturally responsive, and computational-thinking-focused learning that engages children’s heads, hearts, and hands and is supported by dialogic teacher scaffolding. Using a pedagogical framework-building approach, the study conducted a systematic synthesis of relevant learning theories, extant AI literacy frameworks, and empirical studies on early AI learning, yielding three integrated pillars—AI concepts, AI practices, and AI perspectives—and an incremental progression from concrete play toward early symbolic reasoning. For policy and practice, the framework provides a usable roadmap for curriculum design, teacher professional development, and tool selection, while outlining priorities for empirical validation and scalable implementation in future research.

Authors

Institutions

Publication Details

Journal
AI Brain and Child
Published
2026-04-07
DOI
https://doi.org/10.1007/s44436-026-00030-w
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Building early AI literacy: developing a pedagogical framework for early childhood education

Yufeng Qian
AI Brain and Child
Teaching and Learning Programming
article

Building early AI literacy: developing a pedagogical framework for early childhood education

Yufeng Qian
article en

Abstract

This study addresses a pressing need in early childhood education: a comprehensive pedagogical framework for early AI learning. Educators currently lack an instructionally actionable framework that specifies both what to teach and how to teach it for preliterate learners. Grounded in constructivist, constructionist, sociocultural, and embodied learning theories, the study proposes a 3C-3H + DS framework—cognitively appropriate, culturally responsive, and computational-thinking-focused learning that engages children’s heads, hearts, and hands and is supported by dialogic teacher scaffolding. Using a pedagogical framework-building approach, the study conducted a systematic synthesis of relevant learning theories, extant AI literacy frameworks, and empirical studies on early AI learning, yielding three integrated pillars—AI concepts, AI practices, and AI perspectives—and an incremental progression from concrete play toward early symbolic reasoning. For policy and practice, the framework provides a usable roadmap for curriculum design, teacher professional development, and tool selection, while outlining priorities for empirical validation and scalable implementation in future research.

AI Brain and ChildVol. 2(1)
Louisiana State University (US)
Quality Education
Openalex Percentile: Top 6%
Teaching and Learning Programming
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.