Infant motor development has traditionally been studied through child-centered frameworks that often overlook the vital role parents play in shaping early outcomes. This paper provides a renewed ecological approach, foregrounding parental perspectives—knowledge, beliefs, attitudes, theories, and expectations—and examining how they directly and indirectly guide infants’ motor trajectories. Drawing on cross-cultural evidence, we illustrate how differences in parental priorities and caregiving behaviors can either accelerate or delay the emergence of crucial motor skills. We also highlight the reciprocal relationship between parent and child: while parental views shape caregiving practices, children’s developing abilities and behaviors can, in turn, alter their parents’ perspectives. Building on existing theories, including Bronfenbrenner’s ecological systems theory and dynamic systems theory, our integrated model situates the parent–child dyad within broader socioeconomic, cultural, and environmental contexts. This model shows the dynamic, ever-evolving interplay between parents and children and demonstrates the importance of aligning parental cognition with targeted interventions to optimize motor development. By examining how cultural norms, individual experiences, and contextual factors converge, this paper offers both a theoretical framework and practical implications for supporting infants’ growth. This paper will inform future research by encouraging parent-focused developmental studies and guiding practitioners to design culturally informed interventions in the field of motor development.

Infant motor development is traditionally studied through child-centered frameworks that often overlook the vital role parents play in shaping early outcomes. This paper provides a renewed ecological approach, foregrounding parental perspectives—knowledge, beliefs, attitudes, theories, and expectations—and examining how they directly and indirectly guide infants’ motor trajectories. Drawing on cross-cultural evidence, we illustrate how differences in parental priorities and caregiving behaviors can either accelerate or delay the emergence of crucial motor skills. We also highlight the reciprocal relationship between parent and child: while parental views shape caregiving practices, children’s developing abilities and behaviors can, in turn, alter parents’ perspectives. Building on existing theories, including Bronfenbrenner’s ecological systems theory and dynamic systems theory, our integrated model situates the parent-child dyad within broader socioeconomic, cultural, and environmental contexts. This model shows the dynamic, ever-evolving interplay between parents and children and demonstrates the importance of aligning parental cognitions with targeted interventions to optimize motor development. By examining how cultural norms, individual experiences, and contextual factors converge, this paper offers both a theoretical framework and practical implications for supporting infants’ growth through more informed and culturally attuned parental engagement.

Previous studies report differences between mothers and fathers during parent–child interactions. However, the origins of these differences remain unknown. We address this gap by examining the impact of adult gender and gender perceptions on adult-child interactions. Unlike previous studies, we observed both parent and non-parent adults during one-on-one interactions with a child. Further, for non-parent adults the child’s identity was held constant while the child’s assumed gender was actively manipulated using clothing cues. Results reveal systematic differences between parents and non-parents, but also between male and female adults in language quantity, quality, and engagement strategies during adult-child interactions. Adults’ perceptions of gender roles partially explain these findings. In contrast, the child’s gender did not impact adult-child interactions. Together, our results support the notion that male and female adults offer unique contributions to a child’s development.

Within two years, an infant who can barely lift her head learns to sit, reach, and walk. Developmental science found that this growth is both continuous and phased: skills improve smoothly, yet the pace of improvement changes at identifiable ages, and the field built statistical tools to locate those changes. Language models also transform across training, and whether their capabilities grow smoothly or in jumps is actively debated. The present study argues that the developmental answer applies to artificial learners as well, and that the developmental tools can show it. We apply an analysis pipeline built for infant motor development to the training trajectories of 16 open-source language models, each tested at 27 points during training on five benchmarks. The results mirror the infant findings. Growth is continuous but organized into phases, and transitions cluster at a small set of shared points that are deployed differently across domains. Growth proceeds slowly, then rapidly, then slowly, and models differ reliably in developmental rate. Unlike infants, those rate differences are almost entirely explained by model size. The transitions also survive changes of measurement scale, and on a continuous measure they appear earlier and more sharply than on accuracy. The paper contributes a formal test for phase structure in capability growth, a construct mapping between infant development and language model development, and evidence that phase structure may be a shared property of learning systems.

Children increasingly interact with AI systems, yet these systems are designed and evaluated as though every user were a competent adult. A child is not a small adult. Current evaluation asks whether a system avoids harmful outputs, not whether it engages a developing person well. This paper argues that the gap is a measurement problem with fairness and accountability consequences, and it introduces an instrument to address it. The Developmental Interaction Model (DIM) is grounded in decades of parent-child interaction research and organized around three questions that skilled caregivers answer in every exchange: Who is this child? How do I engage? How do I protect and guide? Each question is specified through observable behaviors drawn from validated caregiver instruments, yielding nine evaluable criteria, which are translated into a scenario bank and a scoring sheet modeled on an established caregiver observation instrument. As a first illustration, eight conversations were run with each of six widely used consumer AI systems. The systems recognized the child’s request and usually answered it, but rarely at the child’s developmental level. Several answered in detail where a caregiver would not have, explaining death to a six-year-old or promising a ten-year-old secrecy when her wellbeing was at stake, and some accepted the role of the child’s only confidant rather than bringing in an adult. A one-sentence caregiver profile improved the responses, while a small change in the child’s wording changed whether a system did her homework for her. Since each criterion is tied to a part of the system, these failures point to what would fix them. The paper thus contributes a specification of developmental appropriateness, an instrument for measuring it, and a first application that shows the strengths and limits of current systems. The instrument extends to other topics and settings, laying the groundwork for a benchmark of developmentally appropriate AI for children.