Topic Editors

1. Faculty of Education and Human Development, The Education University of Hong Kong, Hong Kong, China
2. Macquarie School of Education, Macquarie University, Sydney, NSW 2109, Australia
3. Shanghai Institute of Early Childhood Education, Shanghai Normal University, Shanghai 200234, China
Department of Early Childhood Education, The Education University of Hong Kong, Hong Kong, China
National Institute of Education, Nanyang Technological University, Singapore 639798, Singapore
Shanghai Institute of Early Childhood Education, Shanghai Normal University, Shanghai, China

Artificial Intelligence in Early Childhood Education

Abstract submission deadline
3 December 2024
Manuscript submission deadline
1 April 2025
Viewed by
4524

Topic Information

Dear Colleagues,

The world was thoroughly transformed with the launch of ChatGPT in late 2022 and there is now no way to return to pre-ChatGPT times (Su and Yang, 2023). ChatGPT and other forms of generative artificial intelligence (AI) employ algorithms to create new content, including audio, code, images, text, simulations, and videos, and have the potential to drastically change the way in which we live, learn, teach, and work. Early childhood education (ECE) is no exception. Today, young children are growing up in a nearly AI-ubiquitous world (Chen and Lin, 2023). This new wave of generative AI has ignited our hope for better ECE (Yang, 2022) and awakened our fear of its uncertainties (Su and Yang, 2022, 2023). Optimists highlight its benefits for young children and their teachers, whereas pessimists underscore its negative impacts and consequences (Resnick, 2023; Chen and Lin, 2023). While acknowledging that educative and generative AI is a ‘double-edged sword’ (Chen and Lin, 2023), we strongly believe that incorporating it into the ECE sector does have the potential to advance the sustainable development of the field. However, without empirical evidence, it is an impossible task to settle this debate.

Additionally, there is a '3A2S' (accessibility, affordability, accountability, sustainability, and social justice) framework (Luo et al., 2023; Xie and Li, 2020) that has been widely employed to analyze ECE policies and practices. According to Luo et al. (2023), accessibility refers to whether young children are allowed access to educative and generative AI and how to access it in different societies and contexts. Affordability means whether young children and their parents and teachers can afford the use of educative and generative AI and how to maintain affordable access to AI tools for educational institutions and users. Accountability means that leaders, teachers, and parents should responsibly guide, mediate, and monitor the use of AI tools in preschools and at home. There is a need to study how to enhance the accountability of educative and generative AI in the ECE sector. Sustainability concerns whether existing supercomputing capabilities could sustainably support all countries and regions to use educative and generative AI and how to maintain young children's sustainable development. Social justice will address the ‘AI divide’ between those who can access it and those who cannot, the ethical problems related to it, and the fairness for users in non-Western regions. AI might act as a social accelerator, exacerbating the existing gaps between individuals and communities and presenting serious challenges to the sustainable development of the next generation.

To cope with the above challenges and facilitate ECE's sustainable development with AI and AI-powered tools (Vinuesa et al., 2020), we propose this Special Issue to collect empirical studies and theoretical thinking. Topics may include, but are not limited to, the following:

  1. Development and implementation of AI-based ECE policies promoting ‘3A2S’;
  2. Assessment of the effectiveness of AI-based ECE programs in advancing ‘3A2S’;
  3. Exploration of ethical considerations in using AI for sustainable early childhood education;
  4. Investigation of the impact of AI use on young children’s development in diverse contexts and cultures;
  5. Examination of the role of parents and families in supporting AI-based ECE for young children’s sustainable development;
  6. Analysis of the potential of AI in advancing the ‘3A2S’ of early childhood industries.

We welcome submissions of original research articles and reviews that address these research areas and strive for affordability, accessibility, accountability, sustainability, and social justice in the AI-powered ECE sector.

References

Chen, J. J., & Lin, J. C. (2023). Artificial intelligence as a double-edged sword: Wielding the POWER principles to maximize its positive effects and minimize its negative effects. Contemporary Issues in Early Childhood, 14639491231169813.

Luo, W.W., He, H.H., Liu, J., Berson, I.R., Berson, M.J., Zhou, Y.S., & Li, H. (2023): Aladdin’s Genie or Pandora’s Box for Early Childhood Education? Experts Chat on the Roles, Challenges, and Developments of ChatGPT, Early Education and Development, DOI: 10.1080/10409289.2023.2214181.

Resnick, M. (2023). AI and Creative Learning: Concerns, Opportunities, and Choices. https://mres.medium.com/ai-and-creative-learning-concerns-opportunities-and-choices-63b27f16d4d0.

Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 100049.

Su, J., & Yang, W. (2023). Unlocking the Power of ChatGPT: A Framework for Applying Generative AI in Education. ECNU Review of Education. https://0-doi-org.brum.beds.ac.uk/10.1177/20965311231168423.

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., ... & Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 233.

Xie, S., & Li, H. (2020). Accessibility, affordability, accountability, sustainability and social justice of early childhood education in China: A case study of Shenzhen. Children and Youth Services Review, 118, 105359.

Yang, W. (2022). Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation. Computers and Education: Artificial Intelligence, 3, 100061.

Prof. Dr. Philip Hui Li
Dr. Weipeng Yang
Dr. Ibrahim H. Yeter
Dr. Wenwei Luo
Topic Editors

Keywords

  • artificial intelligence
  • early childhood education
  • sustainable development goals
  • AI-based programs
  • educative AI
  • generative AI

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Children
children
2.4 2.7 2014 13.8 Days CHF 2400 Submit
Education Sciences
education
3.0 4.8 2011 24.9 Days CHF 1800 Submit
Sustainability
sustainability
3.9 6.8 2009 18.8 Days CHF 2400 Submit

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Published Papers (2 papers)

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17 pages, 807 KiB  
Article
Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis
by María Consuelo Sáiz-Manzanares, Almudena Solórzano Mulas, María Camino Escolar-Llamazares, Francisco Alcantud Marín, Sandra Rodríguez-Arribas and Rut Velasco-Saiz
Children 2024, 11(4), 381; https://0-doi-org.brum.beds.ac.uk/10.3390/children11040381 - 22 Mar 2024
Viewed by 956
Abstract
Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine [...] Read more.
Advances in technology and artificial intelligence (smart healthcare) open up a range of possibilities for precision intervention in the field of health sciences. The objectives of this study were to analyse the functionality of using supervised (prediction and classification) and unsupervised (clustering) machine learning techniques to analyse results related to the development of functional skills in patients at developmental ages of 0–6 years. We worked with a sample of 113 patients, of whom 49 were cared for in a specific centre for people with motor impairments (Group 1) and 64 were cared for in a specific early care programme for patients with different impairments (Group 2). The results indicated that in Group 1, chronological age predicted the development of functional skills at 85% and in Group 2 at 65%. The classification variable detected was functional development in the upper extremities. Two clusters were detected within each group that allowed us to determine the patterns of functional development in each patient with respect to functional skills. The use of smart healthcare resources has a promising future in the field of early care. However, data recording in web applications needs to be planned, and the automation of results through machine learning techniques is required. Full article
(This article belongs to the Topic Artificial Intelligence in Early Childhood Education)
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18 pages, 2319 KiB  
Article
Monitoring Educational Intervention Programs for Children and Young People with Disabilities through a Web Application
by María Consuelo Sáiz-Manzanares, Raúl Marticorena-Sánchez, M. Camino Escolar-Llamazares and Rut Velasco-Saiz
Educ. Sci. 2024, 14(3), 306; https://0-doi-org.brum.beds.ac.uk/10.3390/educsci14030306 - 14 Mar 2024
Viewed by 900
Abstract
Early care professionals have to use instruments for assessing functional skills in children susceptible to early intervention that apply records and produce developmental profiles and personalized intervention proposals. The aims of the study were (1) to analyze the development of functional skills in [...] Read more.
Early care professionals have to use instruments for assessing functional skills in children susceptible to early intervention that apply records and produce developmental profiles and personalized intervention proposals. The aims of the study were (1) to analyze the development of functional skills in users with an age range of 48–252 months attending school in a therapeutic intervention center for people with motor impairments; and (2) to analyze the development of functional skills in users with different impairments and ages ranging from 7 to 162 months participating in an early outpatient care program. Study 1 applied a sample of 50 users aged between 48 and 252 months all with motor disabilities and Study 2 included a sample of 71 users aged between 7 and 162 months with different disabilities. Factorial and descriptive–correlational designs were applied in both studies. The Student’s t-test for dependent samples, supervised machine learning techniques (linear regression analysis and logarithmic regression analysis), unsupervised machine learning techniques (k-means), ANOVA, and cross-tabulations were used as contrast tests. In Study 1, no significant changes were found in the development of users’ functional skills, except for a decrease in maladaptive behaviors. Likewise, the chronological age variable did not seem to be a determining factor in the results. In Study 2, significant differences were found in the development of all functional skills between the three measurement time points (initial–intermediate–final). In this group, the type of impairment explained 29% and chronological age 40% of the variance in functional development at the final measurement. This study found that intervention before four years old in outpatient mode produced better results in the acquisition of functional skills, with better results in users affected by rare diseases or communication and language delay at ages 49–60 months. Full article
(This article belongs to the Topic Artificial Intelligence in Early Childhood Education)
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