In recent years, rapidly advancing technology has reshaped our world, holding the potential to transform social and economic structures. The United Nations’ Sustainable Development Goals (SDGs) provide a comprehensive roadmap that promotes not only economic growth but also social, environmental, and global sustainability. Meanwhile, artificial intelligence (AI) has emerged as a critical technology contributing to sustainable development by offering solutions to both social and economic challenges. One of the fundamental ideas is that education should always maintain a dynamic structure that supports sustainable development and fosters individuals equipped with sustainability skills. In this study, the impact of various variables related to AI applications in primary education at the elementary school level, in line with sustainable development goals, was evaluated using a mixed meta-method complemented with quantitative analyses. Within the framework of the mixed meta-method, a meta-analysis of data obtained from studies conducted between 2005 and 2025 was performed using the CMA program. The analysis determined a medium effect size of g = 0.51. To validate the meta-analysis results and enhance their content validity, a meta-thematic analysis was conducted, applying content analysis to identify themes and codes. In the final stage of this research, to further support the data obtained through the mixed meta-method, a set of evaluation form questions prepared within the Rasch measurement model framework was administered to primary school teachers. The collected data were analyzed using the FACETS program. The findings from the meta-analysis document review indicated that AI studies in primary education were most commonly applied in mathematics courses. During the meta-thematic analysis process, themes related to the impact of AI applications on learning environments, challenges encountered during implementation, and proposed solutions were identified. The Rasch measurement model process revealed that AI applications were widely used in science and mathematics curricula (FBP-4 and MP-2). Among the evaluators (raters), J2 was identified as the most lenient rater, while J11 was the strictest. When analyzing the AI-related items, the statement “I can help students prepare a presentation describing their surroundings using AI tools” (I17) was identified as the most challenging item, whereas “I understand how to effectively use AI applications in classroom activities” (I14) was found to be the easiest. The results of the analyses indicate that the obtained data are complementary and mutually supportive. The findings of this research are expected to serve as a guide for future studies and applications related to the topic, making significant contributions to the field.

Bu araştırma İlkokullarda Yetiştirme Programında (İYEP) karşılaşılan sorunları, programın paydaşlarına katkılarını ve program sürecinde yaşanan sorunlara yönelik katılımcıların çözüm önerilerini incelemek amacıyla yapılmıştır. Bu amaç doğrultusunda olgubilim deseninde gerçekleştirilen araştırmanın çalışma grubunu 2018-2019 öğretim yılında Kahramanmaraş ili Dulkadiroğlu ve Onikişubat ilçelerindeki ilkokullarda görev yapan 50 gönüllü öğretmen oluşturmuştur. Araştırmanın verileri yarı yapılandırılmış görüşme formu kullanılarak toplanmıştır. Veriler, içerik analizi yöntemi kullanılarak analiz edilmiştir. İYEP’te karşılaşılan sorunlar için elde edilen bulgularda katılımcılar İYEP’te program, mevzuat, öğrenci, rehberlik, fiziki alt yapı, öğretmen merkezli sorunlarla karşılaştıklarını belirtmişlerdir. Programın paydaşlara katıklarına yönelik bulgularda ise öğretmenler İYEP’i uygulayarak öğrencilerin eksik kazanım ve becerilerini tamamladıklarını, öğrencilerle bire bir ilgilenme imkânı bulduklarını, öğrenciler arası seviye farkının azaldığını ifade etmişlerdir. Katılımcılar, İYEP sürecinin daha etkili ve verimli devam edebilmesi için; öğrenci devamsızlıklarının azaltılması, ders materyallerinin geliştirilmesi, öğretmene ödenen ücretin artırılması, hafta içi program faaliyetlerine öğrencilere yeterince dinlenme imkânı verildikten sonra başlanması, paydaşlara programın içeriği, işleyişi ve önemi hakkında yeterli sayıda ve nitelikli bilgilendirmelerin yapılması önerilerini getirmişlerdir.

In this study, a Multi-Complementary Approach (MCA) was employed to assess the effectiveness of Artificial Intelligence-Supported Mind Maps Applications (AISMMA) in a life science course, with the experimental component conducted in a public primary school in Gaziantep, Türkiye. The approach comprised three interconnected stages. In the initial stage, results from the literature were comprehensively analyzed. The analyses revealed that the outcomes of 41 experimental studies produced a strong, positive effect on academic achievement (g = 0.86). In this stage of the research (“post-complementary knowledge”), a pre-test–post-test design was implemented with experimental and control groups comprising 78 primary school students. As a result of this stage, which targeted the identified methodological and contextual gaps, the post-test scores of the AISMMA experimental group were statistically significantly higher (t = 2.85, p < .05, g = 0.68). Finally, in the “complementary knowledge” stage, the outcomes of the first two stages were synthesized, providing a comprehensive examination of AISMMA’s effectiveness in life science instruction. After introducing the MCA and its underlying epistemological rationale, the study’s methodological procedures and findings are presented in a step-by-step manner. A literature review, based on document analysis within the context of “life in our country” in life science curricula, revealed that AISMMA has a positive and significant impact on academic achievement and various supporting variables in life science education. These convergent findings underscore AISMMA’s potential to meet the evolving demands of the digital age, thereby advocating its broader and more systematic integration into life science teaching environments.

This study aims to conduct a comprehensive analysis of the Remedial Curriculum in Primary Schools (RCPS) in state primary schools with varying socio-economic characteristics, based on teachers’ perceptions and experiences. In order to systematically examine the multidimensional structure and implementation dynamics of the RCPS, the CIPP (Context, Input, Process, Product) curriculum evaluation model developed by Stufflebeam was adopted as the conceptual framework for this study. This framework guided the fundamental questions of the research and enabled an in-depth examination of the different dimensions of the curriculum based on teachers' views. The research was conducted using the case study method, one of the qualitative research designs. Data were collected using a semi-structured interview form prepared by the researchers and examined through content analysis. It was determined that the RCPS contributed to the development of students' Turkish and mathematics skills, increased their self-confidence, enhanced their participation in classes, and led to an improvement in their overall academic achievement levels. However, it was also determined that materials are insufficient, parental support is lacking, and the program's schedule causes students to experience fatigue and absenteeism issues. In light of these findings, recommendations such as making the program's schedule more flexible, increasing parent awareness efforts, enriching the material content, integrating game-based activities into the curriculum, developing standardized measurement tools, and strengthening guidance support for teachers will further increase the effectiveness of the RCPS.

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