Workshop on AI for Children: Healthcare, Psychology, Education

Xu Cao, Jintai Chen, Wenqian Ye, Ana Jojic, Sheila Agyeiwaa Owusu, Sheng Li, Megan Coffee, Sicheng Zhao, James Matthew Rehg

Keywords: Healthcare, Psychology, Education

Abstract:

Current AI research and applications often prioritize adult-focused solutions, while progress in AI designed specifically for children's development, health, and education has lagged behind. Our workshop aims to spotlight this issue and bring together researchers from diverse fields to discuss the future of AI design and its applications for children.
In the era of AI, developing bespoke AI systems for children holds special significance:
(i) Advanced AI technologies, such as large language models (LLMs), have the potential to support children’s development, education, and mental health, posing a critical new frontier for research.
(ii) AI in pediatric healthcare is essential, as early diagnosis of childhood diseases can lead to timely interventions, improving prognoses and reducing infant mortality rates.
(iii) AI can also provide valuable tools helping children in low-resource countries, helping bridge gaps in education, healthcare, and other developmental supports.
This workshop will invite researchers from the fields of AI, child psychology, education, pediatrics and social good to discuss how AI, particularly new generative models like LLMs, can address the unique challenges in pediatrics, child psychology, and education. We will also explore the potential risks associated with AI applications for children.
The insights from the workshop's panel discussions will be summarized in a survey paper and submitted to a top-tier journal or AI conference after the workshop.

Submission Number: 33