The Impact of Artificial Intelligence on the Educational Process
Over the past two years, universities have undergone significant changes in their teaching methods due to the introduction of large language models. Both students and instructors actively use AI to solve academic tasks, prompting a re-evaluation of traditional ways of assessing knowledge. Recent studies show that nearly 90% of students use such tools in their daily studies. This presents new challenges: established criteria for independent work are becoming less relevant, and boundaries of acceptable assistance are increasingly blurred. With distance learning and limited online oversight, issues of privacy and transparency are taking on particular importance.
Risks and Gaps in Regulating AI Use
The spread of AI in universities brings not only benefits, but also serious risks. These models can produce incorrect or misleading answers, and the extensive adoption of technology requires universities to reconsider both organizational and technical approaches. An analysis of U.S. higher education practices has revealed an imbalance: while most universities have already developed guidelines for students and instructors, clear instructions for research and administrative staff are often lacking. This leads to a gap between public statements and internal standards. A unified policy covering all categories of participants in the educational process is becoming increasingly necessary.
Causes of failures and solutions
Experts highlight several key factors that affect the effectiveness of AI implementation in universities. These include the quality of educational materials, teaching methods, technical infrastructure, human–machine interaction, and assessment methods. To analyze the issues, an Ishikawa diagram is used, which helps structure the causes of failures across six categories: materials, methods, machines, environment, people, and measurements. This approach makes it possible to identify weak points and determine preventive measures.
Specific solutions are proposed for each category. For example, data security concerns can be addressed by implementing modern protocols and anonymization. Technical limitations can be overcome with cloud platforms and open-source software. To improve collaboration between users and AI, it’s important to develop clear guidelines and train staff.
Step-by-step strategy for implementing AI in universities
Universities can implement AI integration in phases. The first step is an audit: existing practices are analyzed, vulnerabilities are identified, and the level of technology use is assessed. Next, minimum standards are introduced—programs are updated with rules on acceptable forms of assistance, protocols for handling personal data are developed, and requirements for tracking activities are established. The following stage is launching pilot projects in select courses, where new approaches are tested and feedback is gathered. After successful trials, best practices are scaled up, responsibilities are formalized, and policies are regularly updated based on accumulated experience.
This approach enables universities to reduce risks, increase transparency in assessment, and foster a culture of responsible AI use. A flexible implementation system allows decisions to be tailored to different educational programs and scales, while also building trust among all participants in the process.
Opportunities and Challenges for Universities
The integration of artificial intelligence in higher education opens new opportunities for curriculum development and enhances the quality of education. However, achieving positive outcomes requires clear regulations, continuous staff training, and a systematic approach to technology management. Only then can universities ensure the security, efficiency, and transparency of AI use, as well as prepare students for the demands of the digital age.
This material was first published on the Naked Science website.
