AI and Emerging Digital Technologies in Early Childhood Physics Education: A Systematic Review
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Abstract
Background: Young children explore motion, force, light, sound, and material properties through play. Digital technologies may extend these experiences, but evidence on preschool physics learning is dispersed across research. Objective: This systematic literature review synthesizes technologies for physics-related learning among children aged 3–6, concepts and pedagogies, reported outcomes, study designs, and developmental, ethical, accessibility, and implementation considerations. Method: Guided by PRISMA 2020, searches covered Scopus, Web of Science, ERIC, Education Source, and IEEE Xplore, plus backward and forward citation searching. Eligible sources addressed preschool children and AI or emerging technologies in physics or related physical science. Findings were synthesized narratively and thematically, distinguishing direct physics evidence from adjacent research. Results: Twenty-five studies were identified. Technologies included robotics and AI activities, augmented and virtual reality, interactive multimedia, digital games, simulations, and mobile learning. Early-childhood AI research often addresses AI literacy, robotics, and computational thinking; physics-specific outcomes are less consistently reported. Technologies appear promising when paired with prediction, hands-on testing, teacher-guided discussion, and age-appropriate assessment. Conclusion: Emerging technologies can complement physical exploration and educator mediation. Claims about physics learning require measures of physical-science understanding, not engagement or adjacent outcomes. Transparent study-level reporting and quantitative synthesis are needed to strengthen the evidence base.
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