Teaching in the Age of Artificial Intelligence: A Researcher's Approach to Higher Education

When artificial intelligence began confidently solving complex problems, analyzing data, and producing formally correct solutions, higher education faced a fundamental question: what should universities truly assess—the result or the thinking behind it? We spoke with Dr. Maxim Polyakov, PhD in Chemistry, a researcher and educator with more than fifteen years of experience in higher education and scientific research. He is among the academic leaders who view AI not as a threat, but as a tool that exposes the real objectives of education.

Polyakov has built a career with an extensive publication record, a high citation index, active participation in research grants, and mentorship of students in scientific work. This experience, he explains, has shaped a key conviction: in engineering and the natural sciences, the value lies not in the formally correct answer, but in understanding the model, its assumptions, and the limits of its applicability. It is precisely here that artificial intelligence becomes a test of an educational system's maturity.

Artificial Intelligence as an Indicator of Learning Gaps

Polyakov emphasizes that AI does not "break" education—it simply reveals areas where it has long relied on mechanical repetition. If a task can be fully solved with a single query to a model, it indicates that the assignment never demanded analytical thinking in the first place.

In the AI era, testing a student's ability to repeat an algorithm or obtain a numerical result has become largely meaningless. Machines can do this faster and more accurately. The educator's role shifts: to teach students to analyze, interpret, and critically evaluate solutions, regardless of whether they come from a person or a machine.

From Formal Answers to Model Analysis

In his teaching, Polyakov structures learning around the analysis of solution logic. Students must do more than present a correct result—they are expected to explain:

• which assumptions are embedded in the model,
• which factors were simplified or excluded,
• how the solution changes under different boundary conditions,
• where the line lies between applicability and error.

AI in this context serves as a convenient generator of "quick solutions," which then become the subject of analysis. Polyakov underscores that true understanding emerges during this analysis stage. A neural network may provide a correct formula or conclusion, but it cannot explain why a particular approach is appropriate.

Assignments Where Students Cannot Hide Behind AI

Polyakov intentionally designs tasks that cannot be solved with formal answers alone. Typically, these are open-ended problems where the value lies not in the result itself, but in the reasoning behind it.

A common format involves comparing multiple solutions to the same problem, including AI-generated ones. Students are asked to determine which solution is more robust, identify mistakes, and pinpoint which parameters critically influence the outcome. In such conditions, copying loses its purpose: without understanding the physical or chemical context, defending the work is impossible.

According to Polyakov, these formats significantly increase student engagement. They begin asking clarifying questions, testing edge cases, and paying closer attention to the input data.

Critical Thinking as an Engineering Skill

His research experience directly informs Polyakov's pedagogical approach. In scientific work, every result is treated as a hypothesis requiring verification—a principle he applies in the classroom.

Students learn to:

• question "perfect-looking" solutions,
• identify internal contradictions,
• distinguish convenient approximations from physically accurate models,
• understand that error-free calculations do not guarantee correct conclusions.

AI proves especially valuable in this role—as a source of plausible, yet potentially flawed solutions whose analysis develops professional thinking.

Professional Language and Accountability

Polyakov also emphasizes the development of professional language. Neural networks tend to produce generic, impersonal statements that give the illusion of depth. A critical part of learning is refining these drafts: specifying terminology, introducing quantitative assessments, and linking solutions to real conditions.

This skill is essential for future researchers and engineers. In publications, reports, and expert analyses, clarity of reasoning and justification for every conclusion outweigh stylistic polish.

Leadership in Understanding AI in Education

Polyakov's approach to teaching in the AI era is actively discussed within the professional community. He shares his methods with colleagues, speaks at academic events, and participates in expert panels on the future of higher education. His position is grounded in practical experience and outcomes, not abstract technology debates.

He has also introduced initiatives to support faculty and students in integrating AI responsibly into learning, including:

• workshops and seminars on analyzing AI-generated solutions,
• development of curricula that incorporate AI as an analytical tool rather than a shortcut.

Colleagues recognize him as a specialist capable of combining research rigor, methodological precision, and modern digital tools into a coherent educational model.

A Universal Challenge for Universities

Although educational systems vary, Polyakov sees this as a global issue. AI affects universities worldwide because it changes the very nature of working with knowledge. The solution, in his view, is universal: education should focus on thinking rather than rote reproduction.

In this sense, the specific discipline matters less. The approach—analyzing models, verifying assumptions, and taking responsibility for conclusions—applies to any field that demands professional rigor.

The Instructor's Role in a New Academic Reality

Maxim Polyakov's work demonstrates that AI does not diminish the role of the educator; it makes it even more significant. In a world where answers are easily accessible, the value lies with those who teach understanding, verification, and interpretation.

Modern higher education, Polyakov believes, is not about combating technology, but cultivating a culture of thought where AI is a tool, not a substitute for intellectual effort. Such an approach prepares students to operate in conditions of high uncertainty and rapidly evolving technologies.