Child development in educational environments mediated by artificial intelligence: integrating play and formative assessment
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Comprehensive development in early childhood education requires pedagogical approaches that coherently integrate playful experiences, teacher mediation, continuous assessment, and technological resources in ways appropriate to young children. This study aimed to analyze observed changes in overall child development between pretest and posttest measurements and to examine the association between the degree of integration of an educational ecosystem based on pedagogically mediated artificial intelligence, playful learning, and formative assessment and developmental outcomes. A quantitative, analytical, longitudinal study with a single-group pre-experimental design was conducted. Participants were 369 children aged three to five years from 34 classrooms across 18 public educational institutions in Manabí and Los Ríos, Ecuador. The intervention was implemented over 12 weeks and comprised 24 sessions. Overall child development was assessed before and after the intervention, while ecosystem integration was examined through indicators of AI-mediated learning, playful learning, and formative assessment. The mean score increased from 65.29 (SD = 9.39) at pretest to 69.29 (SD = 10.88) at posttest, with a mean difference of 4.00 points, t(368) = 16.83, p < .001, and a large standardized change, d<sub>z</sub> = 0.88. The integration index was positively associated with posttest development (r = .210, p < .001) and developmental gain (r = .205, p < .001). In the adjusted model, the integration index remained positively associated with posttest development, although baseline development was the strongest statistical predictor. The findings indicate a favorable change in overall child development and an association between greater ecosystem integration and better developmental outcomes; however, the single-group design does not support causal attribution of these changes to the intervention
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