TY - GEN
T1 - Technologies for Children's AI Learning
T2 - 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025
AU - Jia, Kaiyue
AU - Yu, Junnan
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/4/26
Y1 - 2025/4/26
N2 - With the growing integration of AI into daily life, various technologies have been developed to teach children about AI. However, differences in their designs highlight the need for a thorough understanding of these tools to make the most of current technological resources and guide the effective development of future learning tools. Through a systematic search, we identified 64 different AI learning tools for children and analyzed their design features, including both static design features (i.e., presentation formats and learning content) and interactive design features (i.e., learning activity types and design features that potentially enhance the effectiveness of the activities). Our findings reveal the current trends and gaps in the design of children's AI learning technologies. Based on these insights, we reflect on future design opportunities and provide recommendations for creating new, effective learning technologies to advance AI education for the next generations.
AB - With the growing integration of AI into daily life, various technologies have been developed to teach children about AI. However, differences in their designs highlight the need for a thorough understanding of these tools to make the most of current technological resources and guide the effective development of future learning tools. Through a systematic search, we identified 64 different AI learning tools for children and analyzed their design features, including both static design features (i.e., presentation formats and learning content) and interactive design features (i.e., learning activity types and design features that potentially enhance the effectiveness of the activities). Our findings reveal the current trends and gaps in the design of children's AI learning technologies. Based on these insights, we reflect on future design opportunities and provide recommendations for creating new, effective learning technologies to advance AI education for the next generations.
KW - AI learning tool
KW - AI literacy
KW - Design
KW - Learning technology
UR - https://www.scopus.com/pages/publications/105005760862
U2 - 10.1145/3706598.3713443
DO - 10.1145/3706598.3713443
M3 - Conference article published in proceeding or book
AN - SCOPUS:105005760862
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 1
EP - 22
BT - CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 26 April 2025 through 1 May 2025
ER -