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Artificial Intelligence Applications in Primary Education: A Quantitatively Complemented Mixed-Meta-Method Study

Year: 2025

Type: article

Study designs: Systematic Review, Meta-Analysis

Abstract: 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

Source: Sustainability

DOI: 10.3390/su17073015

Authors: Yavuz Topkaya, Yunus Doğan, Veli Batdı, Sami Aydın

Institutions: Mustafa Kemal University, Fırat University, Gaziantep University

Language: English

Topic: Artificial Intelligence in Education

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: gold

Estimated APC paid: $2,896