The aim of this study is to evaluate artificial intelligence applications in the preschool education level within the framework of the multi-complementary approach (McA). The McA is designed as a comprehensive approach that encompasses multiple analysis methods. In the first phase of the study, the pre-complementary knowledge process, meta-analysis, and meta-thematic analysis methods were used; in the post-complementary knowledge process, an experimental design with a control group and pre-test/post-test was applied. Finally, in the complementary knowledge phase, the findings of the first two phases were combined, providing an opportunity to evaluate the effectiveness of artificial intelligence applications in preschool education from a more comprehensive and broader perspective. The study provides information about the McA, and then the methodological process and findings of the research are presented in detail within this framework. After providing information about the McA, the methodological process and results of the study are presented step by step within this framework. A literature review based on document analysis in the context of social sciences and teaching in preschool education using artificial intelligence applications has shown that the application of artificial intelligence has positive and significant effects on both student performance and various variables supporting teaching. The complementary results favoring artificial intelligence applications encourage the increased use of such technologies in preschool education, promoting their more widespread and systematic use in the teaching environment.

The aim of this study is to evaluate artificial intelligence applications in the preschool education level within the framework of the Multi-complementary Approach (McA). McA is designed as a comprehensive approach that encompasses multiple analysis methods. In the first phase of the study, the pre-complementary knowledge process, meta-analysis and meta-thematic analysis methods were used; in the post-complementary knowledge process, an experimental design with a control group and pre-test-post-test was applied. Finally, in the complementary knowledge phase, the findings of the first two phases were combined, providing an opportunity to evaluate the effectiveness of artificial intelligence applications in preschool education from a more comprehensive and broader perspective. The study provides information about McA, and then the methodological process and findings of the research are presented in detail within this framework. After providing information about McA, the methodological process and results of the study are presented step by step within this framework. A literature review based on document analysis in the context of social sciences and teaching in preschool education using artificial intelligence applications has shown that the application of artificial intelligence has positive and significant effects on both student performance and various variables supporting teaching. The complementary results favoring artificial intelligence applications encourage the increased use of such technologies in preschool education, promoting their more widespread and systematic use in the teaching environment.

Education systems are undergoing radical transformations under the influence of digital transformation. One of the leading actors in this digital transformation is Artificial Intelligence (AI) applications. This study evaluates the effects of various variables related to AI applications in primary education using a mixed-meta method integrated with quantitative analysis. As part of the mixed-meta method, a meta-analysis of data from studies conducted between 2005 and 2025 was performed using the CMA software. The analysis revealed a moderate effect size of g = 0.51. To ensure the validity of the meta-analysis results and enhance their scope, a meta-thematic analysis was conducted, where content analysis identified themes and codes. In the final phase of the study, an evaluation form prepared within the framework of the Rasch measurement model was administered to primary school teachers to support the findings obtained from the mixed-meta method. The data collected were analyzed using the FACETS software. The study's results indicated that, in the meta-analysis of documents, AI studies were predominantly conducted in mathematics education at the primary level. In the meta-thematic analysis, themes emerged regarding the impact of AI applications on learning environments, challenges encountered during implementation, and proposed solutions. In the Rasch measurement model process, it was observed that AI applications were widely used in science and mathematics curricula (FBP-4 and MP-2). Among the evaluators (raters), J2 was the most lenient scorer, while J11 was the strictest. Regarding the items on the use of AI applications, the statement "I can enable students to prepare a presentation describing their environment using AI tools" (I17) was identified as the most challenging, whereas the statement "I know how to use AI applications effectively in classroom activities" (I14) was the easiest. The findings indicate that the data obtained from the different analysis processes complement and support each other. It is anticipated that the study's results will guide future research and applications in this field and make significant contributions to the domain