Artificial intelligence (AI), which has attracted great attention in recent years, has been widely used in the field of education as in many other fields. AI in education is used to improve student learning, support teachers and provide a more personalized educational experience. AI plays an important role with adaptive learning systems in improving students' learning processes. These systems assess students' individual needs and provide them with appropriate learning materials. AI also monitors students' performance, identifies their weaknesses, and provides additional support in these areas. Thus, students are enabled to learn more effectively and to reveal their full potential. By supporting teachers, AI facilitates classroom management and helps teachers use their time more efficiently. Automated assessment systems allow teachers to quickly assess assignments and exams, while improving the process of providing feedback. In addition, AI also helps teachers understand students' interests and learning styles, so that more personalized instruction can be offered. Another important use of AI in education is student counseling. AI-based counseling systems can guide students in matters such as career choices, university applications, and academic planning. These systems can provide students with viable career options, support the application process, and help them identify their future goals. As a result, the use of AI in education has great potential to improve student learning processes, provide support to teachers and provide a more personalized educational experience. In this study; The subject of AI was examined in a general framework under the title of education and the role of AI in education was discussed. It is thought that AI will contribute to the field by revealing the teacher and how it can be used in the field of education.

The rapidly developing information technologies of our age offer new opportunities in every aspect of our lives. The use of information technologies in educational environments is an important element in creating enriched learning environments. Educational digital games used in teaching-learning environments help students to develop motivation, problem-solving, strategy development skills and creativity. The purpose of this research was to determine the teacher candidates’ state of using digital educational games. The study group of the research consisted of 4th-grade teacher candidates who are studying in the social sciences and classroom teaching departments of Süleyman Demirel University during the 2018/2019 academic year. It has been found that men teacher candidates were more positive than women teacher candidates. Teacher candidates studying social sciences were more positive than those studying classroom teaching. Although teacher candidates were concerned about using educational digital games, they were eager to use these games.

The interaction of education and technology affects the developments occurring in two areas and the development in education increases the speed of technological developments. Educational technology is the functionalization of the scientific knowledge produced in educational sciences and its application into practice. In this research, the content analysis of the studies conducted in Turkey and other countries in the last 5 years is presented in order to determine the current trends in Educational Technology Research Studies. Within the scope of the purposive sample, the studies published between 2015 and 2020 in the field of Educational Technology Research Studies and included in the databases and journals were examined and interpretations were made. Fifteen research criteria were determined within the context of content analysis. These criteria are as follows: index, country, university, department, year of publication, number of authors, research area, method, educational level, sample group, sample number, data collection method, number of bibliographies, analysis techniques and research trends. SPSS 24 program was used in the analysis of the data and the obtained data were presented in the related tables. The data were interpreted based on percentage and frequency. After making general explanations under the tables, the similarities and differences in the studies were analyzed in detail using a meta-analysis method. As a result, it was revealed that the most emphasized issue within the scope of the research area is the adequacy of using Educational Technologies. Keywords: Educational technology research, current trends, meta-analysis.

Teknolojide yaşanan ilerlemelerden biri olan yapay zekâ insan yaşamında önemli gelişmelerden biridir. Buhar makineleri, elektrik, arabalar, bilgisayarlar, internet tarihi gelişim süreci içerisinde birer devrim niteliği taşıdığı gibi, yapay zekâ da yeni bir çağı başlatacak devrim niteliğinde bir teknolojidir. Yapay zekâ temelinde yer alan karmaşık matematikten dolayı doğru yaklaşımlar uygulandığı takdirde bireylerin uygulama yapabileceği disiplinler arası bir bilim dalıdır. Bu çalışmanın amacı; 2017-2021 yılları arasında Bilgisayar Mühendisliği Anabilim Dalında “yapay zekâ” konusunda yapılmış olan yüksek lisans tezlerinin incelenmesidir. Bu çalışma betimsel bir özellik göstermektedir ve veri toplama tekniği olarak doküman incelemesi kullanılmıştır. Veri analizi olarak içerik analizi yapılmıştır. Yükseköğretim Kurulu (YÖK) Ulusal Tez Merkezi veri tabanında 84 yüksek lisans tezine ulaşılmıştır ancak 5 tezin erişim engelinden dolayı 79 tez çalışma sorularına göre analiz edilmiştir. Araştırma sonucunda sağlık, ekonomi, bilgisayar oyunları, verimlilik, optimizasyon, saldırı tespiti, metin madenciliği ve doğal dil işleme, görüntü işleme gibi konularda daha çok yönelim olduğu söylenebilir. Çalışma sonuçlarının araştırma yapacak araştırmacılara rehberlik edeceği düşünülmektedir.

Artificial intelligence (AI) based education represents a significant transformation in the field of education of our age. Artificial intelligence (AI) technology has great potential to enrich the learning experience of special needs students, provide support to teachers, and reduce inequalities in education. Artificial intelligence (AI) technologies can be used to more effectively understand the learning needs of special needs students and provide customized learning experiences for them. Expert systems, adaptive tutorial systems, dialogue-based systems, learning analytics and educational data mining are widely used AI systems developed to increase productivity in educational environments, facilitate achievement of learning goals, provide instant feedback and enrich student interaction. Assessing the learning inclinations, strengths, and weaknesses of students with special needs enables AI to provide tailored learning content and resources that cater to their individual requirements effectively. In this article, tried to present a road map to teachers and researchers by examining the role of AI-based technologies in the education of special needs students, the AI-based technologies that can be used, and the issues to be considered when choosing these technologies.

With the digital transformation in education, big data analytics is increasingly being used to understand, monitor, and improve students' academic performance. Analyzing student behavior, engagement levels, prior achievements, and study habits enables the creation of more effective and personalized learning environments. This study aimed to predict academic achievement from student data using machine learning (ML) algorithms and to identify the factors affecting achievement. Seven different algorithms were implemented for this purpose: SVM, LR, KNN, RF, NB, DT, and LDA. The RF, SVM, and LDA algorithms achieved the highest accuracy rate of 91%. The LDA model was determined to be the most successful model in terms of accuracy and balance performance. Analysis revealed that variables such as class participation, study time, and prior achievement level significantly impact student achievement. The findings demonstrate that self-management, self-regulation, and intrinsic motivation skills play a critical role in academic success. Consequently, machine learning-based models have strong potential for predicting student achievement and identifying at-risk students early. This study highlights the importance of data-driven decision-making processes in education and guides future research on AI-supported applications