The rapid advancement of artificial intelligence technologies is directly affecting all fields. The massive wave brought by AI is pushing culture toward a fundamental transformation in every domain, far beyond previous technological disruptions, while also increasing risks. Therefore, improving AI literacy will not only enhance awareness of its benefits but also its risks.
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.
As in other fields, AI systems bring not only benefits but also risks to education. Beyond concerns about data privacy, the most significant risk is that the content generated by these systems is not always accurate and often contains biases. The phenomenon of AI generating incorrect or fabricated content is commonly referred to as AI hallucination. When AI hallucinates, it produces responses that may appear convincing and internally consistent within the text but are entirely fabricated and disconnected from user input or prior context. As a result, while AI-generated responses may seem reasonable, they can sometimes be misleading or incorrect. For this reason, students must approach automatically generated content with caution. Otherwise, there is a risk of reinforcing the false perception that all information produced by such systems is inherently accurate.
On the other hand, biases present in the training datasets used by these systems during the learning phase are directly reflected in their outputs. In other words, AI systems reproduce biases related to religion, gender, race, culture, and other factors embedded in the data they are trained on. As a result, not all information generated by AI systems can be assumed to be up-to-date, accurate, unbiased, or reliable. Therefore, it is crucial for students and teachers to be highly aware of these limitations and potential risks.
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.
Moreover, such tendencies contribute to the normalization of unethical behaviors among students and undermine respect for effort and hard work. In the long term, this could have serious consequences for the development of a skilled and competent workforce. Therefore, new approaches are needed to accurately measure and evaluate students' performance while accounting for the role of AI systems, ensuring that genuine effort is recognized and fairly assessed.
In this context, a recent study examines the effects of AI on personal security and privacy concerns, loss of decision-making ability, and the tendency toward laziness among a group of higher education students from China and Pakistan. The study’s findings indicate that, within the student sample used, AI applications have a significant impact on students’ loss of decision-making ability and increased laziness. According to the findings, 68.9% of the students’ laziness, 68.6% of personal privacy and security issues, and 27.7% of the loss in decision-making ability can be attributed to the influence of AI applications. Security and privacy concerns are not limited to education; they are widespread across all areas where AI is applied. Moreover, the frequent media coverage of security breaches and privacy violations continues to elevate levels of public concern. The most important solution individuals can adopt in response to these concerns is to continuously improve their literacy regarding these technologies. A lack of knowledge in this area significantly increases the likelihood of data leaks.
The findings of the study indicate that as the functions of AI applications increase and their use in education becomes more widespread, students’ trust in these tools also grows — leading to a rise in student laziness. As students delegate more of their academic tasks to these applications, their reflex to verify the generated content weakens, and they increasingly underutilize their analytical thinking skills and cognitive abilities during the content creation process. Excessive trust in AI-generated content also leads to growing dependency on these tools. Ultimately, this dependency fosters laziness among students. In fact, a similar risk applies to teachers as well.
On the other hand, the study’s findings suggest that the increasing use of AI applications in education in this manner weakens students’ decision-making abilities. This is because, instead of making decisions themselves, students are increasingly adopting the decisions generated by AI applications as substitutes for their own. As a result, their ability to cope with the tensions and complexities inherent in decision-making processes is also deteriorating. In the long term, this vulnerability may indirectly undermine students’ resilience in the face of challenges.
Beyond these effects, as briefly mentioned above, there are two major risks associated with AI-generated content. The first concerns the potential for AI applications to produce biased outputs based on religion, culture, gender, socioeconomic status, race, and other factors. When students place excessive trust in such content, they are less likely to verify it — thus enabling the spread of perspectives that reinforce existing inequalities through the education system. The second involves the risk of hallucination or confabulation behaviors in AI systems. When these behaviors occur, AI applications generate false content that appears credible. If such content is not properly checked and filtered, information that does not correspond to reality can spread more easily through educational channels.
In summary, rather than using these applications as a valuable support to help students fulfill their responsibilities and improve the quality of their work, using them as substitutes for the student’s own efforts can, in the long term, lead to highly negative behavioral outcomes — such as laziness, avoidance of responsibility, and weakened decision-making ability. This is precisely where the critical importance of the responsible use of AI becomes evident. Ethical and responsible use of AI in education can mitigate these negative effects. In this context, both students and teachers should be informed about the potential long-term deterioration in human skills that may result from substituting AI for human effort. Raising awareness of how these tools can be used to complement human capabilities — rather than replace them — and understanding the boundaries of such use is becoming increasingly vital in education. AI technologies in education should not be seen as systems that replace individuals or disregard personal responsibility, but rather as supportive and complementary tools. Ensuring a balanced approach requires ethics-based AI literacy, which is essential for recognizing both the opportunities and risks associated with AI in education. By enhancing AI literacy among students, teachers, and education administrators, the effective and responsible use of these technologies can be promoted while increasing awareness of potential risks, particularly ethical violations.
