Application of Mathematical Concepts and Fully Autonomous Robot to Early Childhood Education
DOI:
https://doi.org/10.21107/njcr.v3i3.294Keywords:
Early Childhood Education, Autonomous Robots, Mathematics, Educational Technology, Resource EfficiencyAbstract
Background of the study: The increasing financial pressures and resource constraints faced by early childhood education (ECE) centers have created a need to explore approaches that may improve the efficiency and sustainability of educational services. Mathematics and fully autonomous robots have potential applications in ECE, particularly in supporting decision-making, resource management, and educational processes. However, the mathematical concepts underlying these applications and the mechanisms through which autonomous robots may be integrated into ECE remain insufficiently synthesized and explained.
Aims and scope of paper: This paper aims to examine the potential applications of mathematical concepts and fully autonomous robots in early childhood education. It also explores trends in early childhood development (ECD) in the United States over the past 50 years through a multivariate regression perspective. The paper focuses on how mathematical approaches and autonomous robotic technologies may contribute to the efficiency and delivery of ECE services.
Methods: The study employed a brief narrative literature review to synthesize existing research on mathematics, autonomous robotics, and their applications in early childhood education. In addition, multivariate regression analysis was used to examine selected ECD trends in the United States over a 50-year period. Relevant literature and historical data were reviewed and analyzed to identify patterns, applications, and potential relationships relevant to ECE efficiency.
Results: The review indicates that mathematical approaches can support several aspects of ECE, including quantitative decision-making, resource allocation, planning, and analysis of educational trends. Fully autonomous robots also show potential for supporting selected educational and operational functions in ECE settings. The regression analysis provides evidence of measurable trends in U.S. ECD over the examined period, although the findings should be interpreted in relation to the available historical data and the limitations of regression-based analysis. Overall, the findings suggest that mathematics and autonomous robotics may offer complementary tools for addressing resource and efficiency challenges in ECE..
Contribution: This paper contributes an interdisciplinary perspective linking mathematics, autonomous robotics, and early childhood education. By synthesizing mathematical applications, robotic mechanisms, and long-term ECD trends, the paper provides an accessible conceptual foundation for policymakers, ECE administrators, teachers, and researchers interested in technology-supported approaches to improving the efficiency and sustainability of early childhood education services.
References
American Economic Association. (2026). EconLit. https://www.aeaweb.org/econlit/
American Psychological Association. (2026). APA PsycNet. https://www.apa.org/pubs/databases/psycnet
Ardoin, N. M., & Bowers, A. W. (2020). Early childhood environmental education: A systematic review of the research literature. Educational Research Review, 31, 100353. https://doi.org/10.1016/j.edurev.2020.100353
Baulos, A. W., Garcia, J. L., & Heckman, J. J. (2024). Perry Preschool at 50: What lessons should be drawn and which criticisms ignored? (NBER Working Paper No. 32972). National Bureau of Economic Research. https://doi.org/10.3386/w32972
Crosby, D. A., Dowsett, C. J., Gennetian, L. A., & Huston, A. C. (2010). A tale of two methods: Comparing regression and instrumental variables estimates of the effects of preschool child care type on the subsequent externalizing behavior of children in low-income families. Developmental Psychology, 46(5), 1030–1048. https://doi.org/10.1037/a0020384
Duncan, G. J., Dowsett, C. J., Claessens, A., Magnuson, K., Huston, A. C., Klebanov, P., Pagani, L. S., Feinstein, L., Engel, M., Brooks-Gunn, J., Sexton, H., Duckworth, K., & Japel, C. (2007). School readiness and later achievement. Developmental Psychology, 43(6), 1428–1446. https://doi.org/10.1037/0012-1649.43.6.1428
García, J. L., Heckman, J. J., Leaf, D. E., & Prados, M. J. (2020). Quantifying the life-cycle benefits of an influential early-childhood program. Journal of Political Economy, 128(7), 2502–2541. https://doi.org/10.1086/705718
García, J. L., Heckman, J. J., & Ronda, V. (2023). The lasting effects of early-childhood education on promoting the skills and social mobility of disadvantaged African Americans and their children. Journal of Political Economy, 131(6), 1477–1506. https://doi.org/10.1086/722936
Goldstein, H. (1986). Efficient statistical modelling of longitudinal data. Annals of Human Biology, 13(2), 129–141. https://doi.org/10.1080/03014468600008271
Gormley, W. T., Jr., Gayer, T., Phillips, D., & Dawson, B. (2005). The effects of universal pre-K on cognitive development. Developmental Psychology, 41(6), 872–884. https://doi.org/10.1037/0012-1649.41.6.872
Hahn, J., Todd, P., & Van der Klaauw, W. (2001). Identification and estimation of treatment effects with a regression-discontinuity design. Econometrica, 69(1), 201–209. https://doi.org/10.1111/1468-0262.00183
Inter-university Consortium for Political and Social Research. (2026). Data excellence. Research impact. University of Michigan. https://www.icpsr.umich.edu/
Jamjoom, A. A. B., Jamjoom, A. M. A., Thomas, J. P., Palmisciano, P., Kerr, K., Collins, J. W., Vayena, E., Stoyanov, D., & Marcus, H. J. (2022). Autonomous surgical robotic systems and the liability dilemma. Frontiers in Surgery, 9, 1015367. https://doi.org/10.3389/fsurg.2022.1015367
Jorda, Ò., & Nechio, F. (2023). Inflation and wage growth since the pandemic. European Economic Review, 156, 104474. https://doi.org/10.1016/j.euroecorev.2023.104474
Ludwig, J., & Miller, D. L. (2007). Does Head Start improve children’s life chances? Evidence from a regression discontinuity design. The Quarterly Journal of Economics, 122(1), 159–208. https://doi.org/10.1162/qjec.122.1.159
National Center for Education Statistics. (2026a). DataLab. U.S. Department of Education. https://nces.ed.gov/datalab/
National Center for Education Statistics. (2026b). Early Childhood Longitudinal Studies Program. U.S. Department of Education. https://nces.ed.gov/ecls/
National Institute of Child Health and Human Development. (2026). The NICHD Study of Early Child Care and Youth Development. National Institutes of Health.
Nelson, B. B., Dudovitz, R. N., Coker, T. R., Barnert, E. S., Biely, C., Li, N., Szilagyi, P. G., Larson, K., Halfon, N., Zimmerman, F. J., & Chung, P. J. (2016). Predictors of poor school readiness in children without developmental delay at age 2. Pediatrics, 138(2), e20154477. https://doi.org/10.1542/peds.2015-4477
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., McGuinness, L. A., Stewart, L. A., Thomas, J., Tricco, A. C., Welch, V. A., Whiting, P., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pew Research Center. (2025). How the U.S. public and AI experts view artificial intelligence. https://www.pewresearch.org/
Pillinger, C., & Vardy, E. J. (2022). The story so far: A systematic review of the dialogic reading literature. Journal of Research in Reading, 45(4), 533–548. https://doi.org/10.1111/1467-9817.12407
Princeton University. (2026). Future of Families and Child Wellbeing Study. https://ffcws.princeton.edu/
Rhim, J., Lee, J.H., Chen, M., & Lim, A. (2021). A deeper look at autonomous vehicle ethics: An integrative ethical decision-making framework to explain moral pluralism. Frontiers in Robotics and AI, 8, 632394. https://doi.org/10.3389/frobt.2021.632394
Sataloff, R. T., Bush, M. L., Chandra, R., Chepeha, D., Rotenberg, B., Fisher, E. W., Goldenberg, D., Hanna, E. Y., Kerschner, J. E., Kraus, D. H., Krouse, J. H., Li, D., Link, M., Lustig, L. R., Selesnick, S. H., Sindwani, R., Smith, R. J., Tysome, J., Weber, P. C., & Welling, D. B. (2021). Systematic and other reviews: Criteria and complexities. Journal of Otolaryngology—Head & Neck Surgery, 50, 41. https://doi.org/10.1186/s40463-021-00527-9
Sterne, J. A. C., Savovic, J., Page, M. J., Elbers, R. G., Blencowe, N. S., Boutron, I., Cates, C. J., Cheng, H.-Y., Corbett, M. S., Eldridge, S. M., Emberson, J. R., Hernán, M. A., Hopewell, S., Hróbjartsson, A., Junqueira, D. R., Jüni, P., Kirkham, J. J., Lasserson, T., Li, T., McAleenan, A., Reeves, B. C., Shepperd, S., Shrier, I., Stewart, L. A., Tilling, K., White, I. R., Whiting, P. F., & Higgins, J. P. T. (2019). RoB 2: A revised tool for assessing risk of bias in randomised trials. BMJ, 366, l4898. https://doi.org/10.1136/bmj.l4898
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Sukhera, J. (2022). Narrative reviews: Flexible, rigorous, and practical. Journal of Graduate Medical Education, 14(4), 414–417. https://doi.org/10.4300/JGME-D-22-00480.1
University of Minnesota Twin Cities. (2026). Chicago Longitudinal Study. Institute of Child Development.
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: Strengths, weaknesses, and recommendations. Chest, 158(1S), S65–S71. https://doi.org/10.1016/j.chest.2020.03.012
Wong, G., Greenhalgh, T., Westhorp, G., Buckingham, J., & Pawson, R. (2013). RAMESES publication standards: Meta-narrative reviews. Journal of Advanced Nursing, 69(5), 987–1004. https://doi.org/10.1111/jan.12092
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Gregory K. Tharp
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The authors publishing in this journal agree to the following terms:
1. Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
