Shaping Primary School Pupils' Information Culture in Artificial Intelligence: Pedagogical Approaches within Supplementary Education

Introduction. Information culture in the field of artificial intelligence is an integrative quality that includes cognitive, critical-reflexive, ethical-legal, practical-activity, and value-semantic components. Despite the active integration of Artificial Intelligence tools into educational practice, a conceptually and methodologically sound system for forming information culture specifically within the context of artificial intelligence is lacking for primary schools: measurable indicators for the key components have not been defined at the level of 8–10 year olds; diagnostic tools are fragmentary and rarely tested for reliability; empirical data on the sustainability of results in the context of supplementary education are limited. The goal of the research is to substantiate and empirically verify a pedagogical model for shaping the specified culture within the supplementary education system for children aged between 8 and 10.
Materials and Methods. A quasi-experiment was conducted at a university-affiliated children’s school. Participants were divided into control and experimental groups. The training module included twelve 60-minute sessions and three checkpoints (baseline, final, and a 5-week delay). Tests and rubric assessment materials for each component, a safety and copyright checklist, and independent double expert evaluation of mini-projects were used. Statistical analysis was performed using nonparametric tests with difference score calculations; reliability was measured using Cronbach’s alpha and Cohen’s kappa.
Results. Upon completion of the module, the experimental group demonstrated a sustained increase in the integrated information culture index and scores across all components, with partial maintenance of this improvement after five weeks. This trend is consistent with the content of the lessons: visual modeling, short “problem setting – testing – improvement” cycles, mini-projects, and regular ethical reflection. Supplemental education creates conditions for age-appropriate acquisition of basic concepts of artificial intelligence and safe practices for its application. At the same time, the risk of the “black box” effect is reduced by systematically checking answers and clarifying limitations.
Discussion and Conclusion. The presented model and diagnostic tools are suitable for implementation in clubs and supplementary education centers; maintenance sessions after 5 weeks are advisable to maintain the results.