Applying Machine Learning to Support Adults in Educating 0- to 36-Month-Old Children at Home: Theories and Experience from Vietnam
This research presents the theoretical bases and practical experiences in Vietnam in applying machine learning to support adults in educating 0- to 36-month-old children at home. The paper also reports the findings of a scientific research project named “Researching and applying machine learning to support adults to educate young children aged 0-36 months in their families” at Vietnam National University, Hanoi. To be more specific, the authors have designed an AI-driven chatbot which acts as a babysitter to provide information and answer frequently-asked questions regarding at-home education of 0- to 36-month-old children following prevalent scripts or data. For simple questions the AI-driven babysitter chatbot will offer the answers on their own. For more complex ones, the chatbot will send them to teachers for solutions. During the process, the chatbot will do self-learning to enhance its ability to give more accurate and practical answers. Experiments show that the chatbot designed by the authors has successfully addressed limitations of the conventional counterparts. Additionally, it also provides accurate and reliable answers, as well as enhanced user experience thanks to trusted references and parents’ coordination in effective child education, ensuring quality and authenticity.
Authors
- Chu Thi Hong Nhung
- Vu Thi Huong Giang
- Lai Thi Yen Ngoc
- Nguyễn Thị Thu Hường (ORCID: https://orcid.org/0009-0009-3938-3831)
- Nguyen Thac Dung (ORCID: https://orcid.org/0000-0002-6832-5472)
- Hoang Thu Huyen
Institutions
- Hanoi National University of Education (VN)
- Vietnam National University, Hanoi (VN)
- Hai Phong University (VN)
- Thuongmai University (VN)
Publication Details
- Journal
- Journal of Posthumanism
- Published
- 2025-04-09
- DOI
- https://doi.org/10.63332/joph.v5i3.705
- Citations
- 1
- Primary Topic
- Education and Vocational Training
- Type
- article
- Field-Weighted Citation Impact
- 8.93