The Effect of Artificial Intelligence-Supported Sustainable Geography Education on the Preparation Process for the IGEO Olympiad

This research aims to examine the effect of artificial intelligence (AI)-supported sustainable geography education on the preparation process for the International Geography Olympiad (IGEO). Research was designed according to the simultaneous triangulation design, which is one of the mixed-methods designs. The research is a quasi-experimental model in terms of revealing the effects of independent variables (IGEO) on dependent variables (artificial). In this study, a quasi-experimental design with a pre-test–post-test control group was used. In this mixed-method study, quantitative data were obtained from questionnaires and achievement tests, while qualitative data were obtained from semi-structured interviews with students and teachers. The quantitative data collection tools used in the study were a mapping literacy achievement test and a problem-solving skills perception scale. The data were obtained from students across various class sections of the same school. Qualitative data were collected through semi-structured individual interview forms, observation forms, participant diaries, and focus group interview forms. Hierarchical regression analysis and ANOVA were used to analyze the statistical data, and the inductive analysis technique was used to analyze the qualitative data. The findings show that AI-supported sustainable geography education improves spatial thinking skills, individualized learning, and learning motivation. In the IGEO exam, students answered the field questions.