Образовательная модель университета в эпоху искусственного интеллекта

Description

Abstract (English)

This article presents a strategic conceptual framework for the fundamental transformation of higher education in response to the rapid advancement of Artificial Intelligence (AI). The authors argue that the traditional "industrial" model of education—which focuses on transferring standardized algorithms and routine professional tasks—is becoming obsolete as AI begins to outperform humans in speed, cost, and accuracy within these domains. The paper proposes a shift toward a new model where the university prepares professionals to solve unique, non-typical problems that lie beyond the capabilities of current AI systems.

Key Conceptual Frameworks:

• The Typology of Labor in the AI Age: The authors categorize human-AI interaction into three types:

  1. Substitution (Automation): Total replacement of humans in highly digitized, routine tasks (e.g., standard engineering calculations, logistics).

  2. Supplementation (Augmentation): Tasks where AI provides technical support, but humans handle social context, ethics, and responsibility (e.g., personalized learning plans or emotional conflict resolution).

  3. Enhancement (Cognitive Symbiosis): The "human + AI" partnership for solving unprecedented problems where no historical data exists. This is identified as the primary target for future university graduates.

The Four-Layer Educational Model:
The proposed curriculum is structured into four interconnected layers rather than traditional disciplinary silos:

1. General Ontology: Worldview, ethics, and the meaning/consequences of human action.

2. Professional Ontology: Mapping specific industries, identifying technological gaps, and understanding the division of labor.

3. Project Methodology: The central layer focused on "thought-activity" cycles, including problem formulation and designing solutions for complex, unique challenges.

4. Tools: Just-in-time acquisition of specific hard and soft skills required for current project stages.

Institutional Transformation:
The article details a radical restructuring of the university's internal systems:

• New Faculty Roles: Moving away from the "universal professor" toward specialized roles: Methodologists, Pedagogical Designers, Seminar Moderators (facilitators of meaning), and "AI Professors" (digital twins of experts providing 24/7 knowledge access).

• Systemic Re-engineering: The model requires changes across eight subsystems, including the Methodological subsystem (replacing disciplines with the four-layer logic), the Economic model (moving away from "teaching hours" to project support), and the Digital Technological subsystem (creating an AI-driven environment for monitoring the student's "digital footprint").

Implementation Strategy:
The authors advocate for a "Strategic Track" (2026) involving pilot programs at specific faculties (Engineering, IT, and Education). The ultimate goal is to move the university from a "knowledge transmitter" to a leader in developing "subjects of development"—specialists capable of designing new activities in conditions of high uncertainty.

Conclusion:
By integrating AI as a full participant in the educational process—both as a tool for students and a support system for teachers—the university can overcome the economic barriers of high-quality personalized education and remain a vital subject of societal development in the AI era.