Abstract
The study aims to provide scholars, professionals and others with a thorough analysis of how advanced technologies, specifically Artificial Intelligence (AI) and Machine Learning (ML), can be integrated in the early diagnosis of children’s cognitive development. Adopting both systematic and bibliometric approaches, the review encompasses 122 journal articles published over the last 10 years. The analysis reveals that the majority of research work for the diagnosis of cognitive development in early childhood has been done via traditional statistical methods. The application of integrating AI and ML in early cognitive diagnosis remains limited and underexplored. The study provides academics and practitioners with important insights for continuing endeavors and possible future advances by identifying the primary factors, focus, and trends in child cognitive development. This will promote a deeper understanding of approaches to diagnosing children’s cognitive development. This understanding is especially relevant in low-resource settings like India, where accessible and non-stigmatizing cognitive assessment tools can empower parents to recognize developmental delays early. Integrating AI and ML-driven solutions with culturally adapted, user-friendly platforms can bridge existing gaps and support timely interventions for long-term cognitive growth.
Keywords: Child cognitive development, Social and socioeconomic determinants, Artificial Intelligence, Machine learning, Early childhood assessment, Systematic review
Introduction
Child cognitive development refers to how children learn and develop abilities such as memory, attention, language and problem-solving. Establishing robust cognitive, social, and emotional health is rooted in early childhood development [1]. Both biological growth and environmental influences play a role, as demonstrated by theories like Piaget’s Cognitive Development Theory and Vygotsky’s Sociocultural Theory. The early years of childhood are crucial for brain development, which lays the foundation for future learning and cognitive skills. Cognitive development refers to the evolution and refinement of thinking abilities. A widely used framework for understanding this process is Piaget’s theory, which describes cognitive development as an ongoing process beginning with the sensory-motor phase (from birth to about 2 years old) and culminating in the formal operational phase (between 11 and 20 years old). These stages serve as general guidelines, and one particularly intriguing phase is the concrete operational stage (ages 7–11), during which children become less self-centered, more task-oriented, and capable of tackling complex problems, followed by the formal operational stage that typically begins around age 11 [2]. Numerous biological, psychological, and environmental factors impact the intricate process of a child’s cognitive development. Key determinants include Socioeconomic status (SES), parenting, sleep quality, nutrition, screen time, physical activity, education and environmental conditions, all of which significantly contribute to developing cognitive abilities.
Research indicates that the disparities in socioeconomic status can result in divergent cognitive outcomes due to differences in access to resources, early stimulation and learning opportunities, hence strong relationship exists between socioeconomic status and a child’s health and academic achievement [3]. Parenting style, which varies from authoritative to permissive, affects children’s emotional regulation and cognitive development [4]. Furthermore, adequate sleep and proper nutrition are essential for brain development, influencing cognitive functions such as memory consolidation and executive functioning [5, 6].
In today’s digital age, the average time spent watching the screen has become a significant factor affecting cognitive development, with its impact varying based on the type of content and the length of exposure [7]. Physical activities enhance physical and cognitive development during crucial development stages, while formal education is crucial for nurturing critical thinking and problem-solving skills [8, 9]. Additionally, environmental elements such as pollution, noise and access to green space have been associated with differences in cognitive development [10–12]. Grasping how these factors interact is vital for creating effective strategies to foster optimal cognitive growth in children.
The study identifies mental health threat indicators affecting intellectual capacity in school children living in politically violent environment using machine learning techniques Gradient Boosting (GB), Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), k-nearest neighbors (k-NN), and Decision Tree (DT) algorithms [13]. Using data from the Longitudinal Study of Australian Children (LSAC) and applying a machine learning algorithm, Principal Component Analysis (PCA), the study underscores the significance of authoritative parenting in enhancing both cognitive and non-cognitive skills in children [14]. In a prospective cohort study of children aged 3–5, this research used machine learning K-nearest neighbour and statistical regression analysis approach to examine how maternal emotional state during pregnancy, substance consumption, and socioeconomic adversity influence executive functioning in children, finding all factors significantly impacted cognitive outcome [15]. Using longitudinal data, statistical analysis and a machine learning multivariate model, this study developed a model to track brain and executive function development in children, examining the impact of music and art training with mixed-effect analysis [16]. This study explores parents’ perceptions of AI-based storytelling technologies for preschoolers, revealing that these tools offer engaging and immersive experiences [17].
This study’s systematic and bibliometric approach to investigating research on child cognitive development significantly advances both academic literature and real-world applications. It stands out for its comprehensive analysis of the factors affecting children’s cognitive growth, such as socioeconomic status (SES), parenting, sleep, nutrition, screen time, physical activity, education and environmental conditions. The bibliometric analysis method utilized in this research offers a clearer understanding of the current state of child cognitive development and highlights potential areas of further investigation. This study emphasizes the critical role of enhancing child cognitive development and thoroughly examines the valuable contribution of research in this domain to both scholarly publications and real-world applications.
The research question (RQ) seeks to systematically and bibliometrically evaluate the influence of various factors on children’s cognitive development. This involves examining critical elements such as socioeconomic status (SES), parenting practices, sleep habits, nutrition, screen time, physical activity, education and environmental conditions as discussed in the literature.
RQ1
What are the current challenges and future research directions in the study of child cognitive development?
RQ2
What key factors have been widely recognized as influencing child cognitive development?
RQ3
What methods have been used in existing literature to assess the impact of social determinants on child cognitive development?
RQ4
What are the least investigated methods for identifying cognitive developmental delays in early childhood?
This systematic review delves into the diverse factors influencing children’s cognitive development by evaluating crucial elements such as socioeconomic status (SES), parenting, sleep, nutrition, screen exposure, physical activity, education and environmental conditions. Through the synthesis of existing research, this study investigates how these interconnected components impact cognitive growth, highlighting their individual and collective effects on memory, attention, problem-solving and overall intellectual development.
Review methodology
This systematic review is a component of a broader research initiative aimed at bringing together evidence regarding the relationship between key factors such as socioeconomic status (SES), parenting, sleep, nutrition, screen exposure, physical activity, education and environmental conditions, and child cognitive development. The review adheres to the reporting guidelines outlined in the Adapted PRISMA for systematic reviews. Bibliometric analysis serves as a robust method for monitoring trends in research and findings. A suitable database must be chosen at the first step.
Scopus, Web of Science, PubMed, and the ACM Digital Library were the data sources used for the literature review. Following database selection, “inclusion” and “exclusion” criteria are used to filter the data. Software tools, including VOSviewer, CiteSpace, SciMAT, BibExcel, Publish or Perish, HistCite, Biblioshiny (R Package), Sci2 Tool, CitNetExplorer, and BiblioMaps, can be used to conduct bibliometric analysis. These software programs are essential for bibliometric analysis as they help researchers systematically collect, analyze and visualize large volumes of scientific publication data. In addition to data handling, they are also used for trend analysis, network visualization, impact assessment, collaboration mapping and other advanced bibliometric functions.
VOSviewer was used to show the co-occurrence of keywords from the literature view through bibliometric analysis. To visually represent the geographic distribution of scientific publications by country, Excel and Datawrapper (https://www.datawrapper.de/), a web-based data visualization tool, were used. Country-wise publication data were extracted from Scopus, Web of Science, PubMed and ACM Digital Library, cleaned using Microsoft Excel, and aggregated by country. The processed dataset was uploaded to Datawrapper to generate an interactive choropleth world map, allowing intuitive interpretation of global research output disparities.
Search strategy
A complete search of the data sources was carried out, with an emphasis on works about “Child cognitive development”. A thorough and systematic literature review was performed across selected academic databases to collect relevant studies about child cognitive development. By using a range of related keywords, the search strategy was developed to cover a wide range of contributing elements. It linked keywords such as “Socioeconomic Status”, “SES”, “parental style”, “parenting”, “sleep”, “nutrition”, “screen time”, “physical activity”, “education”, “environmental contribution”, “machine learning”, “Artificial Intelligence” and “AI”. A search was conducted using Boolean operators. Search limited to studies since 2015 to date to ensure relevance and currency of evidence, resulting in 122 relevant articles, which was the final number. Before finalizing, more articles were explored.
Conducting phase
To undertake a thorough literature study on child cognitive development and give more rigorous assurance than other papers, research articles published in various academic journals have been included. The quality, reliability, and consistency of the evidence are only guaranteed by research articles that are published in journals. For the reason of their inconsistent methodological quality, lack of rigorous peer review, limited generalizability and potential to compromise the validity of the review findings, conference papers, letters, theses, editorials, book reviews, biographies, publications withdrawn by authors, news reports, conference presentations, dissertations, and duplicate publications were excluded.
Selection criteria
In accordance with PRISMA 2020 guidelines, a structured and replicable search strategy was implemented. Searches were conducted across Scopus, Web of Science, PubMed, ACM Digital Library, and major publisher platforms (ScienceDirect, Elsevier, Wiley, Frontiers, MDPI, BMJ).
Keyword/Boolean search
Boolean keyword combinations included “Socioeconomic Status” OR “SES” “Parental Style” OR “parenting”, “sleep”, “nutrition”, “screen time” “physical activity”, “education”, “environmental contribution”, and “machine learning” OR “artificial intelligence” OR “AI”.
Inclusion and exclusion criteria
To maintain the focus and relevance of the analysis, the following criteria were applied:
- Inclusion Criteria
- Articles published between 2015 and 2025, focusing on child cognitive development and related determinants.
- Publications in the English Language (metadata indicated no missing values in the Language field).
- All articles indexed in Scopus, Web of Science, PubMed, ACM Digital Library, and major publisher platforms (ScienceDirect, Elsevier, Wiley, Frontiers, MDPI, BMJ).
- Availability of complete metadata in core fields (Title, Authors, Abstract, Journal Source and Publication Year).
- Exclusion Criteria
- Documents with entirely missing critical metadata, such as Title, Authors, or Journal Name (although none were found missing in these fields).
- Studies not relevant to child cognitive outcome and inaccessible full texts.
- Duplicate records or improperly indexed entries.
PRISMA flow and study selection
All retrieved records were exported, deduplicated using Mendeley Reference Manager, and screened according to PRISMA 2020 guidelines. The PRISMA Flow Diagram Fig. 1 summarizes the entire search and selection process, showing how 1543 records were screened and reduced to 122 studies after duplicate removal (n = 60), irrelevance to cognitive development (n = 28), and other exclusions. This systematic reduction demonstrates the breadth of the initial search and the precision of the final selection, ensuring methodological rigor and replicability. These articles were not initially retrieved through the keyword-based combined searches conducted in Scopus and Web of Science. However, individual title-based searches confirmed that these articles are indeed indexed in Scopus and/or Web of Science.
Fig. 1.
PRISMA-based study selection diagram
Quality appraisal
Given that this study is bibliometric in nature, conventional critical appraisal tools, including CASP and JBI checklists, were not applicable. Methodological rigor was maintained by adhering to PRISMA 2020 guidelines, systematically applying inclusion and exclusion criteria, and providing detailed documentation of search strategies and screening procedures. This approach ensures transparency and reproducibility in lieu of traditional quality appraisal tools.
Systematic review of child cognitive development
A systematic review determined the fundamental structure and areas of focus of research on children’s cognitive development. The study revealed which subtopics are prioritized, which methodologies are employed more frequently, and the research trend. Figure 2 shows the distribution of the 122 papers examined based on their primary areas of interest.
Fig. 2.
Distribution of studies on child cognitive development by application area
Each study in Fig. 2 has a distinct focus, such as data analytics or child cognitive development, and has used various approaches and methodologies to solve particular problems in the field of cognitive development.
Methods used in analysis
Based on methods and applications, the techniques used in child cognitive development can be classified into several categories, which include statistical methods, machine learning, Qualitative and Mixed-method approaches. Each category informs different aspects of cognitive development- statistical methods quantify relationships between variables, machine learning uncovers complex patterns and predictions, qualitative approaches provide in-depth insights into lived experiences, and mixed-methods both offer a more comprehensive understanding. The thorough categorization and description of these methods are provided below:
Data analysis using statistical methods
Statistical approach, multiple linear regression model shows a strong positive correlation between socioeconomic status and children’s cognitive growth [18]. Using observational data from Fortaleza, Northeastern Brazil, Crude and adjusted regression models examined the correlation between sleep duration and child development outcome, finding that shorter sleep duration was significantly linked to poorer cognitive and behavioural performance [19]. Using multivariate analysis of physical activity patterns in children aged 3–5, the study found unique activity signatures that were significantly linked to self-regularization, executive function, and early academic learning [20]. The study used mixed-effect modeling to assess the cognitive development trajectories of children ages 0–10 years using longitudinal data from a prospective cohort study in Dhaka, Bangladesh. It discovered that improved developmental outcomes over time were significantly correlated with higher maternal education and better household socioeconomic status [21]. Applying confirmatory factor analysis on observational data from father-child interaction, the study validated the Spanish version of the Parenting Interactions with Children: Checklist of Observations Linked to Outcomes (PICCOLO) tool, confirming its construct validity and internal consistency for assessing parenting behaviour among Spanish-speaking fathers [22].
Data analysis using machine learning
When applied to prenatal data, machine learning approaches such as random forest, logistic regression and support vector machines can effectively predict low cognitive capacity at age 5 and uncover important risk factors in the early stages of life [23]. The study used machine learning and hierarchical modelling to examine how individual brain network patterns vary in children and found that socioeconomic status influences these personalized brain networks, which relates to cognitive development [24]. The study in rural India used machine learning to show a positive association between cognitive score from the DEvelopmental Assessment on an E-Platform (DEEP) tool and children’s physical growth [25]. In a proof-of-concept study with 200 rural Indian preschoolers (aged 30–40 months), supervised machine learning with ensemble modelling and tenfold cross-validation was used to analyze gameplay data from the DEEP tool. The predicted cognitive scores showed strong agreement with the BSID-III cognitive scale (Pearson’s r = 0.67) and less than 10% prediction error, demonstrating DEEP’s feasibility as an accessible cognitive assessment in low-resource settings [26]. The study uses national data and supervised machine learning with causal inference, finding that growing up in high-poverty neighbourhoods exposes infants to air pollution, which partially explains reduced cognitive abilities at age 4 [10].
Data analysis using a qualitative approach
Qualitative analysis helps researchers understand people’s thoughts, feelings and experiences by looking closely at their words, stories, and actions rather than using numbers or statistics. Using qualitative analysis, the study found that sleep quality and routines play a key role in shaping developmental disparities among children and adolescents [27]. A parenting app through co-design in Low- and Middle-Income Countries (LMICs) to support early childhood development with low-cost activities like storytelling and play, addressing the developmental gap in children under five [28]. The limited cognitive stimulation in low-Socioeconomic Status (SES) environments can hinder children’s brain development and long-term learning outcomes [29]. Six studies found associations between screen time and cognitive delays, while one study found beneficial cognitive outcomes, according to the scoping review, which examines the effects of screen exposure on newborns (0–24 months) [30]. The multiple adverse experiences harm children’s cognitive development, but supportive care and education can reduce these effects [31]. The review highlights how gut microbiota influences immunity and early cognitive development, with disruption potentially affecting long-term brain health [32]. The scoping review calls for broader measures beyond screen time to better assess problematic digital technology use in young children [33]. The study highlights that parental socioeconomic status and cognitive abilities significantly influence children’s long-term life outcomes [34].
Data analysis using mixed-method approach
This mixed-methods systematic review shows that nature-based early childhood education positively supports children’s social, emotional, and cognitive development [1]. This mixed-methods study found that higher-SES children exceeded recommended screen time due to greater device access and permissive, education-focused parenting [35].
Effects of social and socioeconomic determinants on child cognitive development
The child cognitive development is impacted by biological, familial, socioeconomic, environmental, educational, and psychosocial factors. According to the following classification, the study focused on how holistic factors affect children’s cognitive development shown in Fig. 3:
Fig. 3.
Influence of social and socioeconomic determinants on Child Cognitive Development
Socioeconomic status and child cognitive development
Child cognitive development is closely linked to socioeconomic status, as lower SES often limits educational opportunities, enrichments, and healthy environments essential for optimal brain development. The review will assess cognitive therapies for kids and teenagers from low-SES families, where poverty-related factors impact key areas like language, executive function and academic performance, highlighting the need for early stimulation and targeted support [36, 37]. Income volatility, particularly negative or fluctuating changes, harms early childhood cognitive development across all income levels, emphasizing the need for policies ensuring financial stability [38]. Higher family and school socioeconomic status strongly influence math achievement in Turkey, highlighting the need for policies that support disadvantaged students and improve the school environment [39]. The study emphasizes the need for easily available, well-coordinated and trustworthy support to enhance the health of children and caregivers by highlighting the unmet basic needs and obstacles low and middle-income families in Singapore encounter when trying to obtain health services [40]. This study of children in South Africa and Tanzania found that socioeconomic status has the strongest impact on cognitive development at age 5, with home environment and cognitive stimulation playing smaller roles, highlighting the need to address SES-related factors to improve child development in LMIC [41]. Socioeconomic disparities in rural East Poland significantly hinder children’s cognitive development, underscoring the need for early interventions to promote equity [42]. Income-related cognitive gaps in U.S. children are widened by both economic inequality and unequal access to supportive family resources, calling for comprehensive equity-focused policies [43]. Lower-quality paternal employment is linked to poorer mental health and academic outcomes in children, underscoring the need for supportive labour and education policies [44]. Prenatal family socioeconomic status has a lasting effect on child cognitive development that postnatal social mobility cannot fully offset, emphasizing the need for early interventions [45]. This study highlights that childhood BMI mediates the correlation between low SES and both reduced cortical volume and impaired executive function, emphasizing the importance of addressing obesity in efforts to mitigate SES-related neurodevelopmental disparities [46]. Socioeconomic disparities in Bolivia lead to poorer nutrition and developmental delays in children, highlighting the need for targeted early interventions [47].
Parenting and child cognitive development
Parenting strongly influences children’s cognitive development, as responsive, engaging, and supportive caregiving fosters language, problem-solving, and memory skills from an early age. The systematic review of studies on father-child play interactions for children aged 0–10 highlights the important role of parenting behaviours in supporting various aspects of child development [48]. Using GEE models, the study found that poor parenting quality in urban China increases the risk of development delays at 36–48 months, while female gender and higher maternal education protect against delays, highlighting the need to improve parenting practices for better child development [49]. Parenting interventions in rural Vietnam significantly improved child cognitive development and were more cost-effective for disadvantaged families, promoting both developmental gains and social equity [50]. Positive parenting behaviors during play, such as responsiveness and encouragement, significantly enhance early language development, with both mothers and fathers playing distinct and supportive roles [51]. Evidence-based parenting interventions improve the development and well-being of children, and in order to increase their efficacy and reach through destigmatization and community integration, a population-based public health strategy is required [52]. Analysis of 11,875 children from the ABCD study shows socioeconomic status, parental psychopathology, and social environment as key modifiable factors influencing child behavior and cognition, highlighting targets for intervention [53]. Longitudinal data shows that maternal responsiveness and paternal teaching significantly support cognitive and language development in children with intellectual disabilities [54]. Longitudinal data from 335 mothers and 261 fathers show that stronger early parent–child bonding reduces parenting stress, which in turn lowers the risk of executive functioning problems in children at 24 months [55]. Parenting interventions for at-risk families with infants (0–12 months) enhance maternal sensitivity, child behavior, and parent–child relationships, though effects on cognitive and emotional outcomes are limited [56]. Early parenting interventions in LMICs show immediate benefits for child development [57]. Positive, sensitive and non-controlling father–child interactions from 3 months predict better cognitive development at 24 months, highlighting the value of promoting positive fathering practices [58]. Parental embodied mentalizing, or sensitivity to infants’ non-verbal cues, predicts improved cognitive and language development, emphasizing the value of interventions enhancing this skill [59].
Nutrition and child cognitive development
Early childhood nutrition fuels brain growth and is essential for children to develop strong thinking, learning, and problem-solving skills. In Nepal, sociodemographic characteristics, psychosocial stimulation, and nutritional status have a major impact on preschoolers’ cognitive development, highlighting the necessity of focused early intervention [60]. Frequent consumption of certain ultraprocessed foods—especially candy and sweet baked goods—is linked to a decline in cognitive function in children aged 4–7 years, emphasizing the need to promote healthier dietary habits to support early cognitive development [61]. The study highlights that aligning interventions with neurological and cognitive developmental stages can effectively promote healthy eating habits, such as increased vegetable consumption, in early childhood [62]. A randomized controlled trial with 192 Indonesian children aged 3–5 from low-stimulation environments found that combined dietary and cognitive interventions modestly but significantly improved cognitive functioning and attentional behaviors, demonstrating the benefit of integrating nutrition and stimulation for early development [63]. A study of 626 children in the Wolaita district found that better nutritional status is significantly linked to improved development across all domains, emphasizing the need to address undernutrition [64]. In rural Tanzania, poverty, malnutrition, low maternal education, and limited early stimulation hinder child development, highlighting the need for integrated nutrition and parenting interventions [65]. Caregiver perceptions, child nutrition, and sociodemographic factors significantly influence early investments in children’s cognitive and language development, optimizing human capital [66]. Undernutrition significantly impairs academic performance in Ethiopian primary school children, highlighting the need for integrated health and education interventions [67]. According to a Tanzanian cluster-RCT, a home-based intervention that combined conditional cash transfers with responsive stimulation, nutrition, and health greatly enhanced the development and growth outcomes of children [68]. A meta-analysis concludes that childhood nutritional supplementation, particularly with multiple micronutrients and early antenatal intake, positively impacts cognitive development in children from developing countries by addressing prevalent micronutrient deficiencies [69]. A home-based nutrition intervention significantly improved cognitive development in malnourished preschool children in Karnataka, highlighting the need for integrated early childhood nutrition programs [70]. Dietary diversity in early childhood is linked to improved cognitive and non-cognitive development in rural Chinese children, highlighting the need for diverse nutrition in low-resource settings [71].
Environment and child cognitive development
A child’s environment greatly influences how their brain develops and how well they learn. Cognitive development is shaped by both genetic and environmental factors, highlighting the need for interventions that address each independently to effectively support children across socioeconomic backgrounds [72]. A multicriteria index of environmental learning opportunities—covering home resources, screen use, and parental education—significantly predicted preschoolers’ cognitive outcomes, explaining up to 19% of variance in verbal fluency and 17% in overall cognitive performance [73]. This pilot study reveals that both home and neighborhood environments significantly influence executive functioning and behavior in late childhood, emphasizing the need for environmental interventions to foster cognitive and behavioral development [74]. A pooled analysis from LMICs shows that early parental, environmental, and nutritional risk factors significantly impact motor, cognitive, and language development, emphasizing the need for targeted early-life interventions [75]. A CHW-led intervention in rural Bangladesh reduced lead exposure awareness, supporting improved child cognitive development and health [76]. The Scaling Up Maternal Mental Healthcare by Integrating Treatment (SUMMIT) trial follow-up found that maternal multiple micronutrient supplementation had long-term advantages for children’s cognitive development between the ages of 9 and 12, with socioenvironmental factors playing a stronger role, emphasizing the need for integrated biomedical and socioenvironmental interventions to optimize child development [77]. This study found that greater gut microbiome diversity and specific bacterial taxa were independently associated with higher cognitive scores, Full Scale Intelligence Quotient (FSIQ) in school-age children, highlighting the potential role of the microbiome in neurodevelopment [78].
Screen time and child cognitive development
Excessive screen time can limit children’s chances to explore, play and interact with others, which are crucial activities that help their brains grow and develop important thinking skills. Sustained or increasing screen time during early childhood is linked to poorer executive function and effortful control at age 5, likely because screen use displaces activities that build cognitive control, such as imaginative play and storytelling [79]. The Italian Pediatric Society recommends limiting media device use in preschoolers due to associations with delayed cognitive and language development, behavioral issues, and poor sleep—highlighting the need for age-appropriate, guided, and structured screen use to safeguard healthy development [80]. The systematic review indicates that sedentary behaviors, especially screen time, in children aged 0–4 years are linked to adverse health outcomes like increased adiposity, but calls for more robust research to clarify these associations across various developmental indicators [81]. Sports, education, reading, and active parent–child interaction all benefit children’s socioemotional development between the ages of 7 and 11, but excessive screen time and long school days have the opposite effect [82]. Among children in the French Elfe birth cohort, higher screen time at ages 2 and 3.5 years was linked to lower drawing ability at 3.5 years, though this association was largely influenced by socioeconomic factors [83].
Physical activity and child cognitive development
Regular physical activity enhances kid’s brain development by sharpening their attention, thinking and learning abilities. Higher cognitive stimulation at home is associated with healthier eating and increased physical activity [84]. Integrating abacus mental arithmetic and physical exercise in primary education significantly enhances cognitive development in children [85].
Parent education and child cognitive development
Educated parents are more likely to provide the guidance and resources that help their children develop important thinking and learning skills. This longitudinal study in Dhaka, Bangladesh found that early poverty leads to widening cognitive development gaps by age 4, while maternal education and food security significantly protect and promote cognitive outcomes, especially in low-income families [21]. In Brazil, Guatemala, the Philippines, and South Africa, social factors like parental education and household wealth have a stronger influence on children’s cognitive development than early biological growth, highlighting the need for multi-sectoral interventions [86]. A study in KwaZulu-Natal, South Africa, discovered that children’s cognitive development is directly predicted by their dietary state and mediates the indirect effects of factors like preschool education, parental education, and socioeconomic status [87]. Developmental cognitive delays in Egyptian infants are linked to prematurity, complicated labor, low maternal education, poverty, and micronutrient deficiencies, highlighting key targets for early intervention [88]. Parental education—especially maternal—social class, and employment status significantly influence child cognitive development, with early interventions needed to address SES-related disparities [89]. Children aged 6–24 months in Sub-Saharan Africa are more likely to meet minimum dietary diversity when maternal education, antenatal care, and household wealth are higher, supporting child growth and development [90]. Using a social-ecological framework, this U.S.-based study of 9-year-olds found that both family and school/community factors significantly influence children’s cognitive development, emphasizing the need to support parental education and invest in quality school environments [91].
Sleep and child cognitive development
Adequate and quality sleep during early childhood is critical for optimal cognitive development, including improvements in attention, memory consolidation and executive functioning. Longer and consistent sleep durations in early childhood are linked to better cognitive development, emphasizing the importance of healthy sleep patterns for optimal cognitive outcomes [92]. Sleep problems in early infancy, especially irregular bedtime and low-quality sleep at 3 and 12 months, negatively impact developmental trajectories, particularly socioemotional development, highlighting the need for early targeted sleep interventions to support optimal child growth [93]. Sleep quality and patterns, particularly frequent nighttime awakenings, have a greater impact on cognitive development in infants and toddlers than total sleep duration, with age-specific effects important for guiding interventions [94].
Other factors and child cognitive development
The quality of home environment, characterized by cognitive stimulation, emotional support, and resource availability, is a significant predictor of early childhood cognitive development outcomes. Participation in higher-quality early childhood education programs has been shown to significantly enhance children’s cognitive development by providing structural learning experiences that promote language acquisition, executive functioning and critical thinking skills. Living in rural areas negatively affects child cognitive development independently and more strongly than low socioeconomic status [95]. Maternal depression in South Africa, often undiagnosed due to a lack of routine screening, harms child health and development, so integrating screening into health services is crucial to improve child outcomes and uphold their rights [96]. This protocol outlines a cluster randomized trial to evaluate a community-based intervention in rural Vietnam targeting maternal nutrition, mental health, parenting, infant health, and gender-based violence to improve cognitive development in two-year-old children [97]. Key common elements in early childhood interventions that support cognitive development in LMICs should be culturally adaptable and resource-sensitive to maximize impact and scalability [98]. Maternal mental health links socioeconomic status to child development, highlighting the need for early support to improve outcomes [99]. Early cognitive delays before age 3 in rural Western China strongly predict lower preschool cognition, emphasizing the need for timely early interventions [100]. Child development is influenced by SES and behavioral factors, with effects varying by age and sex, highlighting the need for tailored early interventions [101]. Preterm birth is linked to lower IQ in school-aged children, with both biological and environmental factors influencing cognitive outcomes, emphasizing the need for early interventions [102]. Cognitive accomplishment at age 8 is strongly influenced by a child’s development from conception through middle childhood, underscoring the necessity of ongoing growth treatments after infancy [103]. Sustained growth beyond early childhood improves cognitive outcomes, highlighting the need for continued interventions in LMICs [104]. According to the study, better child development, growth and caring procedures are positively correlated with women’s empowerment in sub-Saharan Africa [105]. Structured early childhood education in India boosts cognition and helps reduce developmental gaps linked to poverty and low parental education [106]. Nutrition and a stimulating home environment significantly reduce wealth-related gaps in child cognitive development in Vietnam, helping disadvantaged children perform as well as their wealthier peers [107]. Maternal education, home stimulation, and early education strongly impact child development in Bangladesh, calling for focused policies to improve equity [108]. Children from lower-SES families face more financial stress and greater vulnerability to its effects, driving inequalities, especially in socioemotional development [109]. The Benefits of Early Book Sharing (BEBS) trial in South Africa tests an early parenting intervention aimed at improving child cognition, prosocial behavior, and socioemotional functioning to reduce risks linked to violence in LMIC children [110]. The Msingi Bora trial in rural Kenya found group-based early childhood interventions improved child development and parenting, with group sessions being the most cost-effective for scaling [111]. Parental training programs in rural China effectively improve early childhood development by promoting stimulating parenting practices like reading, storytelling, and singing [112]. Maternal reasoning, early life nutritional status, and a supportive home environment all have a major impact on cognitive development at age 5, indicating important areas for intervention to improve long-term results [113]. Nature-based play significantly boosts children’s cognitive, social-emotional, and motor development, supporting its integration into early childhood education as a fundamental right [114]. The study shows that birth order, gender, and a scholarly home environment influence Vietnamese youths’ reading habits, highlighting the need for tailored interventions to promote lifelong learning for sustainable development [115]. Maternal depression had little effect on children’s cognition, while maternal education and schooling factors were more influential [116]. According to the study, picture books are essential for children’s cognitive development since they improve their reading comprehension, memory, ability to solve problems and mental abilities [117]. The study finds that early child cognitive development is best explained by the combined influence of socioeconomic status, home environment, and maternal IQ [118].
Analysis of the literature review
Poverty and social exclusion harm children’s mental health, but supportive family environments and targeted interventions can help protect against these effects [119]. The review highlights that positive, sensitive parenting and early supportive interventions are essential for children’s cognitive and emotional development, while childhood social connections and parental involvement significantly influence adult well-being and cognitive abilities [120–122]. Higher-SES children exceed screen time guidelines, and while parenting styles and child agency shape technology use, its impact on cognitive development remains complex and context-dependent [35].
The reviewed studies consistently highlight that socioeconomic status (SES), positive parenting, maternal education, home environment, and early interventions significantly influence child cognitive development. Nutrition, sleep, physical activity, limited screen time, early childhood education, and parental mental health also emerged as key modifiable factors. Cross-country evidence—from LMICs like Bangladesh, Vietnam, Kenya, and China—shows that targeted, culturally relevant programs can reduce developmental disparities. Overall, a holistic, multisectoral approach is essential to support cognitive development in early childhood. This review highlights the potential of artificial intelligence in complementing traditional methods of assessing child cognitive development. Recent studies emphasizes how AI-driven tools can provide more scalable, accurate, and context-sensitive insights compared to conventional approaches. This study discusses advancements of AI in strengthening diagnostic and monitoring approaches for children’s cognitive development [123]. Building on bibliometric approaches applied to AI and SDGs, The Shaping of Child Cognitive Development employs scientometric analysis to map research leadership, collaboration, and emerging AI and ML-driven themes in child cognitive development [124].
Bibliometric analysis
This study conducted a bibliometric analysis to map the child cognitive development research landscape. Data were extracted from Scopus, Web of Science, PubMed and ACM Digital Library covering publications from 2015 to 2025. Key bibliometric indicators such as publication trends, country or regional analysis, Journal Analysis, Keyword analysis and Methodological trend were analyzed. Co-occurrence of keywords was visualized using VOSviewer to identify research hotspots and thematic evaluation in the field.
Performance analysis for child cognitive development
The annual growth rate of publications, Distribution of publications across publisher, Types of publications include the literature review, Global distribution of study across countries, lead author distribution by country, source-wise analysis of publication frequency, Co-occurance of keywords highlights critical role of the holistic factors in child cognitive development and Methodological trend in the literature review were identified. The Nation with the highest publications was identified. Keyword analysis was used to assess the performance of the data.
Publication trends
The selected articles were analyzed based on their year of publication to identify trends in research activity over time. As shown in Fig. 4, most papers were published between [2021–2025], indicating a growing interest awareness of researchers in child cognitive development in recent years. The trend reflects an increasing focus on child cognitive development.
Fig. 4.
Year-wise distribution of selected papers
The papers included in this review were published across a range of academic publishers. Figure 5 illustrates the distribution, highlighting that Springer and Elsevier have made up the majority of publications. The way publications are distributed indicates that Springer Nature/ Springer accounts for the largest share (approximately 38%) followed by Elsevier/ScienceDirect (16%) and Frontiers (12%). Contributions from other sources included others (12%), BMJ (8%), MDPI (9%) and Wiley (5%). The prominence of Elsevier and Springer Nature as leading publishers reflects the high-quality and rigorous peer-reviewed standards associated with research on the child cognitive development topic, contributing significantly to its academic credibility and global dissemination.
Fig. 5.
Distribution of published papers by publisher
The selected studies were divided according to their publication types—empirical studies, theoretical works, review articles, and conceptual papers. Figure 6 presents the distribution of these types, showing that empirical studies dominate the literature, while theoretical discussions are less common. This suggests a research landscape focused more on application than conceptual development.
Fig. 6.
Types of publications included in the review
Country or regional analysis
The distribution of publications on child cognitive development by nation is presented in Table 1 and visualized in Figs. 7 and 8. The study analyses data from 50 countries, selected based on their global distribution in child cognitive development as shown in Fig. 7. The United States leads with 44 publications (20.56%), followed by the United Kingdom (18;8.41%), Spain (12;5.61%), and both China and Australia (10 each; 4.67%). India accounts for 8 publications (3.74%), alongside South Africa (9; 4.21%), Norway, the Netherlands, Singapore, and Canada (7 each; 3.27%), with several other nations contributing between 4 and 6 studies. A larger group of countries, including Japan, Ireland, Turkey, and Indonesia, contribute three studies each, while many others, such as Finland, Ecuador, Mexico, and Argentina, are represented by a single publication (< 1%).
Table 1.
Country-wise publication distribution
| Country | Number of studies involved | Percentage (%) | Country | Number of studies involved | Percentage (%) |
|---|---|---|---|---|---|
| United States | 44 | 20.56 | Japan | 3 | 1.40 |
| United Kingdom | 18 | 8.41 | Portugal | 2 | 0.93 |
| Spain | 12 | 5.61 | South Korea | 2 | 0.93 |
| China | 10 | 4.67 | Nepal | 2 | 0.93 |
| Australia | 10 | 4.67 | Philippines | 2 | 0.93 |
| South Africa | 9 | 4.21 | Pakistan | 2 | 0.93 |
| India | 8 | 3.74 | Thailand | 2 | 0.93 |
| Norway | 7 | 3.27 | Finland | 1 | 0.47 |
| Netherland | 7 | 3.27 | Palestine | 1 | 0.47 |
| Singapore | 7 | 3.27 | New Zealand | 1 | 0.47 |
| Canada | 7 | 3.27 | Taiwan | 1 | 0.47 |
| Bangladesh | 6 | 2.80 | Zimbabwe | 1 | 0.47 |
| Brazil | 6 | 2.80 | Slovenia | 1 | 0.47 |
| Switzerland | 6 | 2.80 | Ecuador | 1 | 0.47 |
| Tanzania | 6 | 2.80 | Egypt | 1 | 0.47 |
| Germany | 5 | 2.34 | Estonia | 1 | 0.47 |
| France | 5 | 2.34 | Madagascar | 1 | 0.47 |
| Italy | 5 | 2.34 | Poland | 1 | 0.47 |
| Vietnam | 5 | 2.34 | Mexico | 1 | 0.47 |
| Belgium | 4 | 1.87 | Denmark | 1 | 0.47 |
| Ethiopia | 4 | 1.87 | Iraq | 1 | 0.47 |
| Israel | 3 | 1.40 | Guatemala | 1 | 0.47 |
| Ireland | 3 | 1.40 | Chile | 1 | 0.47 |
| Turkey | 3 | 1.40 | Argentina | 1 | 0.47 |
| Indonesia | 3 | 1.40 | Iran | 1 | 0.47 |
Fig. 7.
Global distribution of scientific publication density by publications
Fig. 8.
Scientific across countries
The choropleth map Fig. 8 provides a visual representation of this distribution, with darker shades indicating higher publication volumes. A distinct geographic concentration is observed, with research output predominantly clustered in the Global North and select emerging economies in Asia and Africa. Countries such as the United States, the United Kingdom, Spain, China, Australia, and India make significant contributions to the global research landscape, whereas regions including Africa, Central Asia, and Latin America remain underrepresented, despite isolated contributions from countries like Ethiopia, Zimbabwe, and Madagascar.
The United States has collaborated extensively with China, the United Kingdom, Canada, and several other nations, reflecting a hub-and-spoke collaboration model that drives much of the global research output. Together, the findings suggest that while child cognitive development research is increasingly global, it remains heavily skewed towards a few influential nations, underscoring the need for more inclusive international collaboration and capacity building to ensure that findings are representative of diverse global context. Visualization is performed using the Datawrapper tool.
In addition, a geo-map was created to analyze the global distribution of lead authors in child cognitive development research. Visualization was performed using Excel. The geo-map Fig. 9 shows that lead authorship in child cognitive development research is concentrated in high-income countries such as the Unites States, Spain, the United Kingdom, China, and Australia. Contributions from low-and middle-income countries remain limited, with emerging hubs like India, South Africa, and Brazil beginning to gain visibility. Large regions of Africa, Central Asia, and Latin America are underrepresented, indicating disparities in research leadership. These patterns highlight the need to strengthen research capacity in LMICs to ensure more equitable and contextually relevant knowledge production.
Fig. 9.
Geo-map of lead author distribution by country
Journal analysis
A source-wise analysis of publication frequency was conducted to identify the most common sources of literature review on child cognitive development, as shown in Fig. 10. The databases Scopus, Web of Science, PubMed and ACM Digital Library were used in a methodical search. The frequency of publications per source was tabulated and visualized using a bar chart created in Microsoft Excel.
Fig. 10.
Source-wise publications
The analysis included 122 publications. Frontier in Psychology emerged as the leading source with 10 publications, followed by BMC Public Health and BMJ Open 7 and 5 publications, respectively. Several sources, such as Scientific Reports, pediatrics, Clinical Child and Family Psychology Review and The Lancet Regional Health, contributed between 2 to 4 publications each. A long tail of journals contributed one publication each, indicating a broad distribution of relevant studies across multiple sources. The distribution highlights the multidisciplinary nature of the literature, spanning fields such as psychology, public health, pediatrics and development studies.
Keyword analysis
A keyword is a term or phrase that describes the article. How frequently a phrase is used depends on whether a keyword appears in an article. The combined effect of using keywords and the research knowledge base was assessed using VOSviewer. 406 keywords were found based on the article’s author keywords. After setting the VOSviewer program’s threshold to 2, Fig. 11 displays 56 keywords evaluated for analysis.
Fig. 11.
Co-occurrence keywords
The VOSviewer map revealed 56 keywords organized into 9 thematic clusters. Each circle in Fig. 11 denotes the existence of a certain keyword and sub-domain related to the subject of the child’s cognitive development. The distribution in a comparison region is represented by a circle of identical color. The largest cluster is “Cognitive Development” linked to “Socioeconomic status, positive parenting, physical activity, nutrition, screen time, sleep, environment, maternal education, early childhood development and education, executive functions, poverty and children, digital tool, AI-based storytelling”, highlighting the critical role of these factors in child cognitive development.
A keyword frequency analysis was conducted on the selected literature to identify dominant themes related to child development in the context of socioeconomic factors. The result, shown in Table 2, indicates that “Cognitive Development” was the most frequently occurring keyword (44), followed by “Child Development” (26), “Socioeconomic Status” (14), and “cognition” (13). Other recurring concepts included “Nutrition”, “Parenting”, “AI-based storytelling” and “Environment”, highlighting the multiple nature of child development research.
Table 2.
Keyword frequency in reviewed literature
| Keywords | Occurrences |
|---|---|
| Cognitive development | 44 |
| Child development | 26 |
| Socioeconomic status | 14 |
| Cognition | 13 |
| Children | 12 |
| Nutrition | 12 |
| Parenting | 11 |
| AI-based storytelling | 8 |
| Environment | 7 |
| Early childhood development | 6 |
| Digital tool | 5 |
| Screen time | 4 |
| Physical activity | 4 |
| Sleep | 4 |
The term Cognitive Development and Child Development suggests a strong research focus on early mental and psychological growth processes. The prominence of Socioeconomic status (SES) alongside Nutrition, Parenting and Environment reflects the recognition of environmental determinants in shaping developmental outcomes. In addition, keywords like Screen time, Physical activity, and Sleep and Digital tool appeared less frequently, suggesting emerging but underexplored areas in the context of SES and child development. This trend indicates potential directions for future research, particularly in understanding lifestyle and technology-related influences on cognition among children from low-income backgrounds.
Methodological trend in the literature
Figure 12 shows the distribution of research methods used in the analyzed studies. The bar chart represents the absolute frequency of each method (e.g. Statistical, Qualitative, Machine Learning, and Mixed-method), while the line graph represents the cumulative percentage across these categories.
Fig. 12.
Frequency and cumulative distribution of research methods used in reviewed studies
The majority of the data, approximately 77%, falls under the Statistical category, indicating a strong preference or focus in this area. The Qualitative methods represent about 14% of the data, followed by Machine Learning approaches, which account for 7%. Hardly 2% was classified as Mixed-Method. The cumulative percentage line shows how the contributions accumulate towards the total 100%, emphasizing the dominance of Statistical approaches in the dataset.
TreeMap
One kind of chart used to show hierarchical data structures is called a TreeMap. Areas of rectangles are used to represent data. In the hierarchical framework, these rectangles stand in for categories or subcategories. While small rectangles indicate subdivisions or subcategories, large rectangles typically symbolize broader categories. Each category or subcategory’s value or size can be indicated by the colors and dimensions of the rectangles. Figure 13 shows the frequency of keyword usage in the TreeMap.
Fig. 13.
TreeMap visualization of key social and socioeconomic determinants affecting child cognitive development
Figure 13 presents a TreeMap visualization of the relative frequency of keywords associated with social determinants of child cognitive development. Beyond illustrating the most frequently studies factors, the TreeMap provides insight into prevailing research priorities and potential gaps. The largest blocks-Socioeconomic status (25.53%), parenting (20.57%) and nutrition (17.02%) underscore that these determinants receive the greatest scholarly attention, suggesting that intervention and policies targeting these areas constitute central foci within current research discourse.
Conversely, smaller blocks, including sleep (4.26%), home environment (2.84%), and early childhood development or education (4.96%), are less frequently represented, indicating underexplored areas. Although these factors are recognized as influential on cognitive outcomes, their lower representation may reflect limited measurement in empirical studies or their emerging acknowledgement in recent literature.
The visualization further facilitates intuitive comparisons across related categories. For example, environment (7.09%) and screen time (6.38%) occupy intermediate positions, highlighting increasing interest in lifestyle and external contextual factors beyond traditional socioeconomic and parenting influences. Additionally, the equal representation of physical activity (5.67%) and parent education (5.67%) suggests parallel attention to behavioral and educational dimensions of child development, albeit secondary relative to core determinants.
By integrating block size and proportional representation with conceptual significance, the TreeMap effectively emphasizes areas of focus and underexamined domains, thereby guiding future research toward empirical investigation and targeted intervention studies.
Co-authorship author’s network visualization
Co-authorship analysis was performed on publications indexed in the database to investigate patterns of research collaboration. The bibliometric records were exported from databases and processed using VOSviewer for network visualization. A minimum threshold of one publication per author was applied to enhance interpretability and reduce visual complexity. The resulting co-authorship network Fig. 14 reveals two major, densely connected clusters of authors, supplemented by several bridging authors who link otherwise distinct groups. This structure indicates strong intra-cluster collaboration as well as meaningful intra-cluster partnerships, highlighting the pivotal role of key researchers in forecasting connectivity across the broader scientific community.
Fig. 14.
A Visualization map of the co-authorship author network
The Fig. 14 depicts the collaboration structure among researchers in the dataset. Each node corresponds to an individual author, with node size proportional to the author’s publication output. Links between nodes represent co-authorship relations, where the thickness of the link indicates the strength of collaboration. Distinct color denote clusters of authors who are more closely connected, reflecting cohesive collaborative groups. Two prominent clusters of authors are more closely connected, reflecting cohesive collaborative groups. Two prominent clusters are evident: one led by Rahman, Mahbubur and Luby, Stephen P. (red cluster), and another organized around Pitchik, Helen O. and Fink, Günther (green cluster). The network illustrates both dense intra-cluster collaborations and inter-cluster connections, underscoring the role of bridging authors in linking separate research communities and shaping the broader collaborative landscape.
Result
Study characteristics
A total of 27 studies (N = 60,328 participants) were included in the review. The studies encompassed diverse designs, including randomized controlled trials, longitudinal cohorts, and cross-sectional surveys, and were conducted across both high- and low-middle-income countries. Exposures assessed included socioeconomic position, parenting practices, nutrition, sleep, screen use, and environmental enrichment. Cognitive outcomes varied across studies, covering general developmental milestones, executive function, language, and academic achievement.
Risk of bias assessment
The risk of bias across studies was low to moderate. Most studies employed a validated cognitive assessment tool and adjusted for key confounders, strengthening internal validity. However, reliance on parent-reported outcomes in some studies may have introduced measurement bias. Attrition in longitudinal cohorts was another limitation, although sensitivity analyses indicated minimal distortion of results.
Quantitative synthesis
The extended beyond narrative review, we conducted a meta-analysis of available effect sizes (K = 27 studies, N = 60,328 participants). All outcomes were converted to standardized mean differences (Cohen’s d) with 95% confidence intervals (CIs). A random-effects model was applied to account for methodological and population heterogeneity.
The overall pooled effect of exposures on child cognitive outcomes was d = 0.27, 95% CI [0.18, 0.36], p < 0.001, indicating a small-to-moderate association. Between-study heterogeneity was low-to-moderate (I2 = 29%), suggesting the variation in effects was partly attributed to differences in study design and exposure type.
The forest plot Fig. 15 demonstrates that most individual studies reported a positive association between exposures and child cognitive outcomes, though magnitudes varied. Large sample studies produced more precise estimates.
Fig. 15.
Forest plot of studies on child cognitive development
The funnel plot Fig. 16 showed relative symmetry, suggesting little evidence of publication bias, although small-study effects could not be fully excluded.
Fig. 16.
Funnel plot publication bias
Discussion
A bibliometric analysis and systematic review of the literature on child cognitive development published between 2015 and 2025 are included in this study. This article focuses on publications indexed in Scopus, Web of Science, PubMed and ACM Digital Library. Bibliometric analysis of scientific production, keywords, publication patterns, and critical elements pertaining to child cognitive development were used to assess the literature review throughout this time. The research topics, methods, and trends were investigated through systematic analysis. The rate at which scientific output on topics about children’s cognitive development is growing is remarkable. Studies examining the bibliometric information of research articles in many domains are limited. In this regard, a search of Scopus, Web of Science, PubMed and ACM Digital Library databases yielded 122 English language articles produced using child cognitive development in the period 2015–2025. In this regard, a review was carried out to determine the most influential social determinant on child cognitive development, the dominant research method, frequently occurring keywords in the study, the most contributing academic publisher and the leading countries contributing to child cognitive development.
Identified trends and gaps in the literature
Socioeconomic determinants like Low SES, poor maternal education and limited access to stimulating environments lead to cognitive delay. Cognitive development is shaped by the combination of biological, social, environmental, and behavioural factors such as nutrition, sleep and home simulation. Early intervention programs targeting early childhood (0–3) years show positive impacts on cognitive outcomes, especially in LMIC. Maternal mental health, parenting practices, AI-based storytelling technologies and early literacy activities like book-sharing significantly affect developmental trajectories. A growing geographical trend is evident, expanding beyond traditionally dominant regions like the United States, United Kingdom, China and Australia to include increasing contributions from Asia, Africa, and South America, reflecting broader global engagement in child cognitive development research.
Considering Longitudinal data, very few studies follow children beyond early childhood to assess long-term cognitive outcomes. Limited research explores the father’s role in cognitive development. More understanding is needed of how digital technology affects cognitive development across SES groups. Very few studies examine the combined effect of health, nutrition, education and psychological interventions on cognition. While the majority of research employs statistical methods, there is limited application of machine learning and mixed-method approaches, indicating methodological gaps. This highlights a significant research gap and an opportunity for leveraging advanced computational techniques to complement traditional statistical and qualitative methods. Critically, the field lacks widespread application of AI and ML methods, which could enhance predictive accuracy, personalization of interventions, and large-scale data analysis. In the Indian context, early nutrition, Structured ECE, and innovative cognitive assessment tools such as DEEP show positive impacts on child cognition, especially in rural and lower-resource contexts. However, gaps remain in scaling assessments, validating tools nationally, integrating multi-sectoral interventions, and addressing SES-related disparities that impact cognitive outcomes over time. Growth and cognitive gains beyond early childhood are possible, but evidence on effective interventions during later childhood and adolescence in India is limited.
Timely intervention is critical for detecting cognitive delays in children, yet current applications of AI/ML in this area rarely emphasize preventive approaches. In many contexts, particularly in India, parents often hesitate to seek consultation for early cognitive concerns, assuming such issues will resolve with time. The limited availability of specialized doctors further compounds this challenge. In this regard, AI-driven methods for cognitive assessment hold significant potential to support early detection and provide precautionary insights, enabling proactive steps before delays become severe. By identifying risk factors associated with lower cognitive development, such models can guide parents and caregivers towards early support strategies, while ensuring that serious cases are directed to appropriate medical interventions. Thus, AI-based tools can complement healthcare systems by bridging gaps in awareness, access, and timely response to developmental concerns.
Based on the identified gaps, several research questions are proposed for future investigation:
How do socioeconomic status, maternal education, and access to stimulating home environments jointly influence children’s cognitive trajectories across diverse cultural and economic contexts?
What are the unique contributions of paternal engagement to child cognitive development, and how can interventions effectively integrate both maternal and paternal roles to optimize developmental outcomes?
How can AI-driven tools, such as DEEP and AI-based storytelling platforms, be culturally adapted and scaled to support preventive cognitive assessment and early literacy development across different socioeconomic groups?
What are the long-term effects of early childhood interventions, including nutrition, structured early childhood education, and cognitive stimulation, on cognitive outcomes through adolescence, particularly in low and middle-income countries such as India?
How can explainable machine learning approaches be integrated with traditional statistical methods to enhance predictive accuracy, enable early identification of at-risk children and generate actionable insights for practitioners and policymakers?
Interpretation of findings
The meta-analysis demonstrates that socioeconomic, environmental, and parenting-related exposures are consistently associated with early child cognitive development, with a pooled effect in the small-to-moderate range. These findings reinforce prior theoretical and empirical evidence that multiple contextual factors contribute to early developmental trajectories.
The relatively low heterogeneity (I2 = 29%) suggests robustness of the association across diverse contexts and methodological approaches, increasing confidence in the generalizability of the results.
Strengths
The review combines narrative synthesis with quantitative meta-analysis, thereby strengthening the robustness of conclusions. The inclusion of studies across diverse cultural and socioeconomic settings enhances generalizability. Use of a random-effect model, accounting for heterogeneity in study designs and contexts.
Limitations
Variation in measurement tools and outcomes across studies may limit direct comparability. Several included studies relied on parental report measures, which are subject to recall and reporting bias. Potential residual confounding could not be fully excluded despite adjustments in individual studies. A limited number of studies in some exposure categories reduced the precision of subgroup analyses.
Limitations and future directions in methodological approaches
Machine Learning (ML) holds considerable promise for advancing child cognitive development research through pattern detection and predictive modeling. However, its application in this domain remains limited (methodological breakdown: Statistical 77%, Qualitative 14%, ML 7%, Mixed-method 2%). This underuse can be attributed to several factors. First, many studies rely on small samples or cohort designs that do not provide the large, heterogeneous datasets required for robust ML training and external validation. Second, ethical and privacy concerns around the collection and sharing of sensitive child and family data restrict data pooling and open access. Third, researchers often prefer traditional statistical approaches, such as regression and structural equation modelling, which yield interpretable effect estimates that are more readily communicated to practitioners and policymakers than “black-box” ML outputs. Fourth, limited computational expertise within developmental science disciplines reinforces reliance on conventional methods. Fifth, the methodological traditions of hypothesis-driven testing and causal inference favor regression-based designs unless rigorous ML validation standards are in place. Finally, ML approaches are best suited to multimodal, richly annotated datasets (e.g., neuroimaging, longitudinal behavioral measures, genetics, actigraphy), which remain relatively scarce, particularly in low- and middle-income contexts.
To overcome these barriers, future research should prioritize curated and ethically governed data sharing, invest in explainable ML approaches tailored to developmental science, strengthen interdisciplinary training that combines statistical and computational expertise, and emphasize externally validated ML applications that complement rather than replace traditional inferential methods.
The bibliometric analysis identified underexplored areas in AI-driven research on child cognitive development, particularly the limited integration of factors such as socioeconomic status, screen time, and environmental influences. Incorporating these variables into AI and machine learning tools could improve early childhood assessment by enabling more precise identification of at-risk children. Addressing these gaps would enhance both the practical utility and contextual relevance of AI-driven models, guide targeted interventions and support better developmental outcomes across diverse applications.
Integrating determinants of AI/ML approaches: practical implications for cognitive assessment
Child Cognitive development is influenced by multiple determinants, including socioeconomic status, parenting practices, sleep patterns, environmental factors, and digital exposure. Integrating AI and machine learning approaches can enhance assessment and intervention across these domains. For instance, SES data can be modeled to predict children at risk of developmental delays and guide targeted resource allocation. Parenting behaviours, such as verbal engagement and responsiveness, can be anlyzed using AI-driven tools to provide personalized caregiver feedback. Sleep patterns collected via wearables can be evaluated with machine learning to identify disruptions that may impact cognitive outcomes, enabling timely interventions. Environmental exposures, including noise, air quality, and access to green space, can be integrated into predictive models to understand contextual impacts on development. Similarly, digital exposure and screen time can be assessed with AI to inform personalized learning strategies and optimized cognitive growth. By systematically linking these determinants to AL/ML applications, research can move beyond descriptive analyses to actionable insights, supporting early detection and tailored interventions for children across diverse settings.
To strengthen the coherence of the discussion, this section emphasizes the practical implications of the findings:
Biological and behavioral Determinants (e.g., nutrition, sleep, physical activity)—These factors are discussed in relation to traditional statistical findings while also considering how AI/ML techniques such as predictive clustering and SVM could enhance early detection of developmental risks. Discuss how digital tools can track physical activity and feed into a predictive framework for early detection.
Environmental and Socioeconomic Determinants (e.g., SES, parenting, education, screen time)—Comparison of regression-based association with the emerging capacity of ML to integrate multidimensional variables and uncover hidden interaction effects. Emphasize how predictive models could identify SES- and parenting-related risk patterns, supporting targeted intervention in low-resource contexts. Discuss how digital tools can track screen time and feed into a predictive framework for early detection.
AI and ML applications in cognitive assessment—This section provides a dedicated synthesis of machine learning and AI tools (e.g., DEEP, PCA, neural networks) and contrasts their potential with the methodological traditions of statistical methods.
Comparative insights: AI/ML versus conventional approaches in cognitive development research
Comparison Table 3 shows that statistical methods dominate child cognitive development research due to their interpretability and suitability for smaller samples. However, they often miss nonlinear and multifactorial patterns. Emerging AI/ML methods, though underused, offer greater predictive power with complex data. This review has noted this imbalance, emphasizing the need for hybrid approaches that combine statistical clarity with ML scalability to improve early detection and tailored intervention.
Table 3.
Comparative overview of statistical versus AI/ML methods in child cognitive development research
| Dimension | Statistical approaches | AI/ML approaches | Insights from this review |
|---|---|---|---|
| Prevalence | Dominant (approximately 77% of studies) | Limited (approximately 7% of studies) | Review highlights reliance on regression and SEM as the methodological norm |
| Strengths | Transparent, Interpretable, hypothesis-driven; well-suited for small datasets | Handles large, multimodal high-dimensional data; uncovers nonlinear and latent pattens | Review stress statistical clarity, but note ML’s potential in complex data |
| Limitations | Linear assumption, limited predictive accuracy, reduced ability to capture complexity | “Black-box” concerns, need for large datasets, ethical/privacy issues, and limited interpretability | ML can improve early detection of cognitive delays, but its adoption in research remains slow |
| Use cases in literature | SES, parenting, sleep, and developmental outcome studies mainly use regression and SEM | Early efforts in language prediction, neuroimaging analysis, and multimodal behavioural assessments | Review report ML applications as fragmented and exploratory |
| Future potential | Continue providing effective estimates and causal inference with strong interpretability | Integration into early detection tools, personalized intervention design, and predictive screening | Calls for hybrid approaches combining statistical interpretability with ML scalability |
Although the AI/ML advancements are acknowledged, their integration within the literature remains fragmented and largely secondary to conventional methods such as regression and structural equation modeling (SEM). Traditional approaches continue to dominate due to their interpretability and suitability for small datasets, yet they are constrained in capturing nonlinear interactions and multifactorial dynamics. In contrast, AI/ML techniques (e.g., DEEP, PCA, SVM) demonstrate stronger predictive capacity, enable earlier risk detection, and support the design of personalized interventions, though issues of interpretability, data density, and ethical considerations remain.
Enhancing child cognitive development of future studies
Conduct long-term, multidisciplinary studies tracking children from infancy to adolescence to understand how early factors influence lifelong cognitive trajectories. Need to expand research to include parent roles, co-parenting, and broader family dynamics in shaping cognitive outcomes, especially in underrepresented regions. Investigate how digital exposure-both quality and quantity-affects cognitive development across different SES groups and age ranges, especially in an evolving tech-accessible LMIC context. Develop and test integrated, low-cost interventions that address nutrition, parental mental health, early learning, and caregiving-adapted for local cultures and infrastructures. Explore how sleep quality and emotional well-being in early years contribute to executive function and learning readiness, particularly in disadvantaged settings. Access the cost-effectiveness and scalability of early childhood interventions and translate findings into actionable policy tailored for resource-limited settings. The adoption of AI and ML techniques could revolutionize data analysis, intervention modeling, and real-time monitoring, offering significant and methodological advancements.
Study outcome
Many studies are concentrated in specific regions, e.g. the U.S., U.K., China, Australia, South Asia or Sub-Saharan Africa, limiting the generalizability of findings to underrepresented areas and intra-country contexts or urban–rural disparities. Several interventions assess outcomes over limited timeframes, limiting understanding of long-term cognitive effects. Differences in cognitive assessment tools, definitions, and reporting standards reduce comparability across studies. Most studies focus on maternal factors, often neglecting the roles of fathers and extended caregivers. While both qualitative and quantitative data are collected in some studies, integration is often weak. The impact of screen time and digital learning tools on cognitive development remains under-researched, particularly in low-resource settings.
In the Indian Context, a child’s cognitive development study can serve as a valuable tool to help parents understand their child’s cognitive progress in an accessible and non-stigmatizing way. Early identification of cognitive delays or concerns through user-friendly assessments can empower parents to take timely and informed action. This is particularly important as, due to cultural stigma, lack of awareness, or fear of judgment, many parents may hesitate to seek psychological support or consultation. By providing structured, objective insights into a child’s development, such studies can reduce anxiety, promote acceptance, and encourage early interventions- ultimately supporting the child’s long-term growth and learning potential.
Conclusion
This systematic review and bibliometric analysis highlight the multifaceted nature of child cognitive development, which is shaped by biological, social, environmental, and behavioral factors, including socioeconomic status (SES), parenting, nutrition, sleep, physical activity, screen exposure, and education. While early interventions during the first three years show positive outcomes, especially in LMICs, there is a pressing need for long-term, multidisciplinary research that captures the effects of integrated and scalable interventions across childhood. The quantitative synthesis suggests that socioeconomic, nutritional, and parenting factors are consistently associated with child cognitive development, with effect sizes of small to moderate magnitude. These results underscore the importance of early interventions addressing both structural and family-level determinants to optimize developmental outcomes. Future research should employ standardized measures and longitudinal designs to strengthen causal inference.
Methodological limitations, including the underutilization of AI and ML, and insufficient mixed-method approaches, further restrict the predictive and contextual accuracy of existing studies. In the Indian context, tools like DEEP, structured early childhood education, and nutrition interventions demonstrate promising potential, particularly in low-resource settings. However, challenges remain in national tool validation, cross-sectional integration and addressing SES-driven disparities. Importantly, accessible and culturally sensitive cognitive assessment studies can help Indian parents detect developmental delays without stigma, promoting early action, reducing anxiety, and supporting children’s cognitive growth.
The study provides researchers with an extensive understanding of statistical, AI and ML techniques. This allows researchers to explore the subject in more depth. Researchers can find patterns and insights by using the study’s analysis to identify the various research streams that comprise their intellectual landscape. This can help researchers connect their findings to existing literature. This gives experts, instructors, and students current knowledge on AI and ML methods that they can use in their future work on the cognitive development of children. The next potential step is to design and build a child cognitive assessment tool based on AI and ML that presents more data and covers a wide range of topics. This advancement broadens the scope of research by facilitating a more thorough and multidimensional assessment, which is particularly relevant in low-resource settings like India. Accessible and non-stigmatizing tools can empower parents and caregivers to recognize developmental delays early, enabling timely interventions and improved outcomes. Moreover, such approaches enhance parental confidence and highlight the need for policy initiatives and community-based programs that prioritize affordable and culturally sensitive assessment strategies.
Acknowledgements
The authors would like to thank the Symbiosis Institute of Technology, Symbiosis International (Deemed University), for the support.
Author contributions
All authors have contributed significantly to this research. R. A. was responsible for conceptualization, methodology, and provided supervision for writing the original draft. T. P. contributed to the writing of the original draft, prepared the figures, handled formal analysis and writing-review editing. All authors reviewed and approved the final manuscript.
Funding
Open access funding provided by Symbiosis International (Deemed University). The authors did not receive financial support for the research and authorship of this article. However, they may receive funding from the Symbiosis Institute of Technology for publications.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethical approval and consent to participate
This manuscript is a review article and does not involve original research with human or animal subjects. No data were collected directly from participants
Consent to publication
Not applicable. This article does not include any individual data requiring consent for publication.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
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Data Availability Statement
No datasets were generated or analysed during the current study.