Education systems are among the primary fields facing the challenges posed by AI technology. This challenge for education systems is twofold. The first dimension concerns the transformation occurring within the education system itself due to AI technologies, while the second dimension relates to how human capital will be trained for existing or new professions in response to the rapid changes AI technologies bring to skill sets in labor markets. Both challenges place significant pressure on education systems. This article focuses solely on the impact of AI technologies on the first dimension within the education system.
Especially with generative AI technologies like ChatGPT and DeepSeek, large language models (LLMs) can now generate content, organize texts in different formats, and translate between languages. As a result, the options available to teachers for enriching educational environments with AI-assisted supplementary materials have significantly expanded and diversified. Teachers can rapidly generate various types of content related to their lessons and use them in classroom settings. In this new landscape, the workload of teachers is undergoing a fundamental shift. While conventional tasks are decreasing, they must focus more on the individual development of each student. Additionally, there is the potential to transform the educational environment from a traditional setting into a much more innovative and interactive learning space.
Thanks to this technology, teachers can automatically evaluate assignments and provide feedback to students much more quickly. Although there are still many shortcomings, AI systems offer various options for assessing students' assignments and exams. In particular, open-ended questions can be generated, and rapid feedback can be obtained. Moreover, automated or semi-automated assessment systems can be developed to provide feedback aimed at improving students' learning outcomes.
On the other hand, findings from AI applications outside the field of education suggest that such technologies improve the productivity of low-performing employees in businesses, thereby narrowing the performance gap between low- and high-performing workers and tightening the productivity scale. A similar effect is expected to apply to education systems as well. In other words, AI-driven tools have the potential to reduce one of the most significant challenges in education: the disparity in students' academic achievement. In this context, personalized learning emerges as a key solution. With AI support, students can access supplementary content tailored to address their weaknesses under the guidance of their teachers, enabling educators to closely monitor each student's individual progress.
Generative AI systems are also influencing foreign language education. Not only are students gaining access to a wider range of support platforms for learning new languages, but teachers also have more opportunities to enrich their instructional materials. In particular, AI-powered tools for correcting texts in foreign languages are becoming widely used, with ChatGPT further expanding these options. Similarly, AI systems are opening new frontiers in artistic fields such as poetry, music, and visual arts, enhancing the potential to enrich education in these areas as well.
Similarly, education administrators also have increased opportunities to leverage AI in closely monitoring educational processes, enabling early intervention and effective guidance. However, the most significant risk in this context is the potential for biased outcomes that could further deepen existing inequalities. Therefore, when making projections, education administrators must remain aware of these risks and always approach AI-generated results with caution and critical evaluation.
One of the major challenges in the use of AI systems in education is the frequent deviation from ethical principles. A particularly concerning issue is that students may rely entirely on these systems to complete their assignments and projects, leading to various forms of academic distortion. First, this type of ethical violation creates a misleading assessment, making underperforming students appear successful, which results in inaccurate measurement and evaluation. In other words, students who use these systems unethically may risk being rewarded unfairly. Second, when a student appears successful despite lacking the necessary competencies, their actual struggles remain hidden. As a result, they may advance through educational stages without receiving early intervention or remediation opportunities.
A Guest Editorial