Artificial intelligence (AI), using algorithms for prediction and analysis, offers significant potential for education. AI in Education (AIEd) focuses on developing AI systems to support cognitive tasks, particularly learning and problem-solving. AIEd applications are emerging in various forms, including personalized learning platforms, AI-powered learning management systems, interactive technologies, and chatbots. While AI offers opportunities to enhance education, challenges exist in implementation and understanding its potential. This study addresses the lack of research on AI's impact on Turkish education, particularly considering the geopolitical and economic factors influencing AI adoption in the country.

Existing literature reveals AIEd applications across various areas: personalized learning, dialog systems, data mining for student analysis, smart agents, chatbots, AI-based evaluation systems, and administrative support within schools and universities. AIEd approaches are categorized into knowledge-based, data-based, and logic-based methods. Intelligent Educational Systems (IES) represent a key area of AIEd, providing personalized and adaptive learning environments. The study also reviews international AI initiatives in education, highlighting successful examples from countries such as China, the United States, and the UK. These examples involve AI-powered adaptive learning platforms, intelligent tutoring systems, and personalized learning tools. The review notes the increasing use of AI in education, especially in automating assessment, providing personalized feedback, and streamlining administrative tasks.

The study employed a qualitative research design using data from Istinye University and Yildiz Technical University, comprising 7950 records with 10 attributes. Data collection involved interviews, focus groups, and surveys of students and instructors. Data analysis included data mining techniques, focusing on identifying unknown trends and patterns using clustering, classification, and prediction methods. The study also examined computer-based education (CBE) systems and their relationship to AIEd, noting the overlap between these systems. The use of deep learning and recurrent neural networks (RNNs) for data analysis is described. The methodology involved using a 70-30% split for training and testing the machine learning model. Different classification methods, including machine learning, AI recognition systems, swarm intelligence, and ensemble methods, were compared using metrics such as precision, recall, F-score, accuracy, and Kappa statistics. The study also investigated two types of uncertainty: aleatoric and epistemic.

The study's key findings include: A positive outlook among faculty members on AI's role in education; Optimistic predictions for AI's future in education, encompassing improved learning, assessment, student support, and virtual learning environments; A need for faculty training to effectively utilize AI tools. The ensemble machine learning model achieved high accuracy (0.95), precision (0.93), recall (0.95), and F1-score (0.94) in classifying student performance. The results showed a decline in the level of understanding of AI applications among educators, emphasizing the necessity for more awareness-raising initiatives. Analysis of Turkish AI policy revealed a contrast between statist and market-based approaches, with the private sector playing a prominent role in AI development.

The findings highlight the potential benefits of AI in Turkish education, while simultaneously underscoring the existing gap in knowledge and awareness surrounding AI applications. The optimistic view of AI's future role underscores the potential for transforming the educational landscape. However, the necessity for comprehensive faculty training is crucial to ensure the effective integration and utilization of AI tools. The high accuracy of the predictive model suggests its potential for identifying students at risk, allowing for proactive interventions. Turkey's unique blend of statist and market-driven approaches to AI necessitates further exploration of their interactions and implications for education.

This study contributes to understanding the potential of AI in Turkish education, revealing both opportunities and challenges. Further research should investigate the long-term impacts of AI on student learning outcomes, teacher professional development needs, and the ethical considerations of AI implementation. Addressing the identified knowledge gap through targeted training and awareness initiatives will be crucial to successfully integrating AI in the Turkish educational system.

The study's limitations include its reliance on a qualitative approach, which may limit the generalizability of findings. The sample size, while substantial, may not fully represent the diversity of opinions within the Turkish educational landscape. Future studies could incorporate quantitative methods to broaden the understanding of AI adoption.