AI-SUPPORTED LEARNING IN SECONDARY CHEMISTRY EDUCATION

The relevance of this study lies in the growing demand for developingstudents’ functional literacy as a key educational outcome in secondary chemistryeducation. Functional literacy is understood as students’ ability to apply chemicalknowledge to real-life contexts, while artifi cial intelligence and digital tools areconsidered supportive instructional means rather than independent educational goals.The purpose of this research is to investigate the eff ectiveness of artifi cial intelligence–based digital tools in supporting the development of functional literacy during thestudy of electrolytic dissociation in secondary school chemistry. The study employeda quasi-experimental design and was conducted in a comprehensive secondary school.Two groups participated: an experimental group taught using artifi cial intelligence–supported digital tools and a control group instructed through traditional teachingmethods. Student learning outcomes were assessed over a three-month periodusing diagnostic and formative assessment tools. The results demonstrate that theexperimental group showed a greater improvement in performance, increasing from 16.5 to 22.4 points, compared to the control group, which improved from 14.2 to17.8 points. The fi ndings indicate that adaptive feedback, visual representations, andindividualized learning pathways facilitated deeper conceptual understanding andmore meaningful learning experiences. Despite the limited duration and sample size,which classify the study as a pilot investigation, the results highlight the potential ofartifi cial intelligence and digital tools to support functional literacy development inchemistry education. The practical signifi cance of the study lies in its implicationsfor instructional design, curriculum planning, and future large-scale research on theintegration of artifi cial intelligence in school science education