The Theoretical and Methodological Opportunities Afforded by Guided Play With Young Children
Open accessAbstract
For infants and young children, learning takes place all the time and everywhere. How children learn best both in and out of school has been a long-standing topic of debate in education, cognitive development, and cognitive science. Recently, guided play has been proposed as an integrative approach for thinking about learning as a child-led, adult-assisted playful activity. The interactive and dynamic nature of guided play presents theoretical and methodological challenges and opportunities. Drawing upon research from multiple disciplines, we discuss the integration of cutting-edge computational modeling and data science tools to address some of these challenges, and highlight avenues towards an empirically grounded, computationally precise and ecologically valid framework of guided play.
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Caregivers can supply AI systems with targeted contextual information because they best understand a child's cognitive level.
“As those who best understand a child's cognitive level, caregivers can also supply AI systems with targeted contextual information [23].”
From A Systematic Review of Generative AI in K–12: Mapping Goals, Activities, Roles, and Outcomes via the 3P Model (2025) · cites this paper as [23]
The Introduction says adult guidance should be tailored to a child’s needs and skill level. The Coupling Computational Models and Data Science Tools section says data from expert teachers have been used to train algorithms, but it does not say caregivers best understand a child’s cognitive level or can supply AI systems with targeted contextual information. (LLM, conf=0.78)
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Lessons from infant learning for unsupervised machine learning
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- Authors
- Yu, Y., Shafto, P., Bonawitz, E., Yang, S. C.-H., Golinkoff, R. M., Corriveau, K. H., Hirsh-Pasek, K., & Xu, F.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- University of Delaware (US)
- Boston University (US)
- University of California, Berkeley (US)
- Corresponding author
- Yu, Yue
- ORCID
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2018-07-17
- Volume
- 9
- Pages
- 1152-1152
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC6057112
- OpenAlex
- W2883110088
- Cited by
- 59
- References
- 77
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 8 · 2023: 12 · 2022: 11 · 2021: 13 · 2020: 4 · 2019: 1
- FWCI
- 14.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- OpenBU (Boston University) (publishedVersion, other-oa)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- TUScholarShare (Temple University) (submittedVersion, cc-by)
- Funders
- National Science Foundation (SMA-1640816)
- Topics
- Social Sciences › Education › Early Childhood Education and Development
- Social Sciences › Psychology › Developmental and Educational Psychology › Child and Animal Learning Development
- Social Sciences › Education › Child Development and Digital Technology
- Keywords
- guided play, young children, children's learning, child-led learning, computational modeling, data science, cognitive development, play-based learning
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