Systematic review reveals early childhood AI literacy shaped by robot capabilities, not child development
Early childhood AI literacy shaped by robots, not child development, review finds
A review of 11 studies finds early childhood AI literacy definitions align with robot capabilities rather than child developmental needs. Researchers highlight a Concept-Practice-Perspective framework, but caution against abstract concepts in young learners.
Source: Robotics — Robot learning news discovery · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
number of records initially identified
178
View original evidence
ished between January 1, 2020 and November 5, 2025, with the final database searches conducted on November 6, 2025. The initial search yielded 178 records. After removing 56 duplicates, two researchers independently screened the titles and abstracts of 122 records, with 84 considered potentially relevant and advanced to full-text review. Screening agreement was high, with a Cohen’s kappa of 0.92. Ultimately, 11 revie
Open source S4number of duplicates removed
56
View original evidence
ished between January 1, 2020 and November 5, 2025, with the final database searches conducted on November 6, 2025. The initial search yielded 178 records. After removing 56 duplicates, two researchers independently screened the titles and abstracts of 122 records, with 84 considered potentially relevant and advanced to full-text review. Screening agreement was high, with a Cohen’s kappa of 0.92. Ultimately, 11 revie
Open source S4number of records screened for titles and abstracts
122
View original evidence
ished between January 1, 2020 and November 5, 2025, with the final database searches conducted on November 6, 2025. The initial search yielded 178 records. After removing 56 duplicates, two researchers independently screened the titles and abstracts of 122 records, with 84 considered potentially relevant and advanced to full-text review. Screening agreement was high, with a Cohen’s kappa of 0.92. Ultimately, 11 revie
Open source S4number of records considered potentially relevant
84
View original evidence
ished between January 1, 2020 and November 5, 2025, with the final database searches conducted on November 6, 2025. The initial search yielded 178 records. After removing 56 duplicates, two researchers independently screened the titles and abstracts of 122 records, with 84 considered potentially relevant and advanced to full-text review. Screening agreement was high, with a Cohen’s kappa of 0.92. Ultimately, 11 revie
Open source S4number of review articles formally included
11
View original evidence
ished between January 1, 2020 and November 5, 2025, with the final database searches conducted on November 6, 2025. The initial search yielded 178 records. After removing 56 duplicates, two researchers independently screened the titles and abstracts of 122 records, with 84 considered potentially relevant and advanced to full-text review. Screening agreement was high, with a Cohen’s kappa of 0.92. Ultimately, 11 revie
Open source S4number of studies coded independently
11
View original evidence
w articles met all inclusion criteria and were formally included in the synthesis. The researchers coded the 11 studies independently, focusing on three core elements: how early childhood AI literacy is defined and which dimensions it includes, the main themes explored, and the pedagogies used and their effects. After initial coding, the two researchers cross-checked results through two online discussions and one off
Open source S5number of studies with thematic analysis
11
View original evidence
d who can use a generative AI tool to draw a picture does not necessarily have the cognitive maturity to understand the algorithm behind it. Thematic analysis of the 11 studies revealed four core research areas. AI cognition and conceptual understanding is the most widely investigated theme, addressed in all 11 studies, focusing on how young children comprehend basic AI concepts, with research indicating that early A
Open source S13
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ished between January 1, 2020 and November 5, 2025, with the final database searches conducted on November 6, 2025. The initial search yielded 178 records. After removing 56 duplicates, two researchers independently screened the titles and abstracts of 122 records, with 84 considered potentially relevant and advanced to full-text review. Screening agreement was high, with a Cohen’s kappa of 0.92. Ultimately, 11 revie
Open source S4
w articles met all inclusion criteria and were formally included in the synthesis. The researchers coded the 11 studies independently, focusing on three core elements: how early childhood AI literacy is defined and which dimensions it includes, the main themes explored, and the pedagogies used and their effects. After initial coding, the two researchers cross-checked results through two online discussions and one off
Open source S5
d who can use a generative AI tool to draw a picture does not necessarily have the cognitive maturity to understand the algorithm behind it. Thematic analysis of the 11 studies revealed four core research areas. AI cognition and conceptual understanding is the most widely investigated theme, addressed in all 11 studies, focusing on how young children comprehend basic AI concepts, with research indicating that early A
Open source S13
widely used pedagogy, which lets children experience AI concepts firsthand through hands-on activities with programmable robots, AI-enabled toys and simple AI tools; storytelling, which integrates AI concepts and ethics into age-appropriate narratives; constructivist teaching, which enables children to actively construct AI knowledge through independent exploration and collaborative tasks; gamification, which embeds
Open source S17
Source：Robotics — Robot learning news discovery · bioengineer.org