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Related Experiment Video

Updated: Jan 9, 2026

A Common Marmoset Model of Mother-Infant Intervention for Breastfeeding Disorders in the Presence of Paternal Inhibition and Maternal Neglect
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Increasing breastfeeding literacy: A preliminary study to develop an AI-based chatbot.

Kamila Rosamilia Kantovitz1,2, Maria Eduarda Mattoso2, Maria Davoli Meyer2

  • 1School of Dentistry, University of Maryland, Baltimore, MD, USA.

International Journal of Nursing Sciences
|December 10, 2025
PubMed
Summary

This study developed and validated BabyChat, an AI tool to boost breastfeeding knowledge for mothers and caregivers. BabyChat offers educational content on exclusive breastfeeding benefits and strategies, showing promise for improving infant nutrition literacy.

Keywords:
Artificial intelligenceBreast feedingCommunicationEducationKnowledge

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Area of Science:

  • Public Health and Nutrition
  • Artificial Intelligence in Healthcare
  • Maternal and Child Health

Background:

  • Exclusive breastfeeding (EBF) is crucial for infant development but faces low adherence rates, especially among low-income populations.
  • Existing educational methods struggle to effectively reach and engage mothers and caregivers with vital breastfeeding information.
  • There is a need for innovative, accessible solutions to enhance breastfeeding literacy and support optimal infant feeding practices.

Purpose of the Study:

  • To develop an AI-powered educational solution, BabyChat, designed to increase breastfeeding literacy among mothers and caregivers.
  • To validate the functionality and content relevance of the AI-based chatbot for delivering accurate breastfeeding information.
  • To assess user experience and expert opinions on the chatbot's effectiveness in educating about exclusive breastfeeding.

Main Methods:

  • BabyChat was developed in two phases: content creation (Canvas, MindMeister, ManyChat) focusing on WHO-recommended EBF guidelines and functionality testing (UserTesting).
  • Content validity was assessed using the Content Validity Index (CVI) by healthcare professionals evaluating clarity and relevance on a 4-point scale.
  • User feedback from mothers and expert validation from psychology, dentistry, and medicine professionals were collected to evaluate the chatbot.

Main Results:

  • BabyChat's content covered 8 topics and 18 subtopics related to breastfeeding, including nutrition and weaning strategies.
  • Most mothers found the chatbot's Q&A clear, helpful, and accurate, though some noted occasional issues with misinterpreted questions or interface.
  • Expert validation showed excellent content relevance, with agreement rates between 25%-100% and median CVI values of 3-4.

Conclusions:

  • The AI-based BabyChat was successfully developed and validated as a tool to enhance breastfeeding literacy among mothers and caregivers.
  • BabyChat demonstrates potential for improving infant nutrition education and supporting breastfeeding practices.
  • Future research should expand validation to nursing staff and incorporate culturally specific topics for broader impact.