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Education Paradigm Shift To Maintain Human Competitive Advantage Over AI

Stanislav Selitskiy, Chihiro Inoue

TL;DR

The paper analyzes how generative AI, especially LLMs, disrupts traditional intellectual labor and education, identifying both empirical weaknesses and deep linguistic-technical limitations of current models. It advocates a shift from knowledge transmission to constructivist, Piaget-Vygotsky-inspired education, reanimating Humboldtian university ideals and introducing Thought-Action-based activity-organisational games to align learning with human strengths. The work outlines a transition plan emphasizing open-ended, collaborative, and adaptive assessment, reducing reliance on standardized testing, and integrating AI as a tool rather than a competitor. Its significance lies in proposing a principled, implementable framework to preserve human competitive advantage in an AI-pervasive knowledge economy, with practical guidance for educators, administrators, and policymakers.

Abstract

Discussion about the replacement of intellectual human labour by ``thinking machines'' has been present in the public and expert discourse since the creation of Artificial Intelligence (AI) as an idea and terminology since the middle of the twentieth century. Until recently, it was more of a hypothetical concern. However, in recent years, with the rise of Generative AI, especially Large Language Models (LLM), and particularly with the widespread popularity of the ChatGPT model, that concern became practical. Many domains of human intellectual labour have to adapt to the new AI tools that give humans new functionality and opportunity, but also question the viability and necessity of some human work that used to be considered intellectual yet has now become an easily automatable commodity. Education, unexpectedly, has now become burdened by an especially crucial role of charting long-range strategies for discovering viable human skills that would guarantee their place in the world of the ubiquitous use of AI in the intellectual sphere. We highlight weaknesses of the current AI and, especially, of its LLM-based core, show that root causes of LLMs' weaknesses are unfixable by the current technologies, and propose directions in the constructivist paradigm for the changes in Education that ensure long-term advantages of humans over AI tools.

Education Paradigm Shift To Maintain Human Competitive Advantage Over AI

TL;DR

The paper analyzes how generative AI, especially LLMs, disrupts traditional intellectual labor and education, identifying both empirical weaknesses and deep linguistic-technical limitations of current models. It advocates a shift from knowledge transmission to constructivist, Piaget-Vygotsky-inspired education, reanimating Humboldtian university ideals and introducing Thought-Action-based activity-organisational games to align learning with human strengths. The work outlines a transition plan emphasizing open-ended, collaborative, and adaptive assessment, reducing reliance on standardized testing, and integrating AI as a tool rather than a competitor. Its significance lies in proposing a principled, implementable framework to preserve human competitive advantage in an AI-pervasive knowledge economy, with practical guidance for educators, administrators, and policymakers.

Abstract

Discussion about the replacement of intellectual human labour by ``thinking machines'' has been present in the public and expert discourse since the creation of Artificial Intelligence (AI) as an idea and terminology since the middle of the twentieth century. Until recently, it was more of a hypothetical concern. However, in recent years, with the rise of Generative AI, especially Large Language Models (LLM), and particularly with the widespread popularity of the ChatGPT model, that concern became practical. Many domains of human intellectual labour have to adapt to the new AI tools that give humans new functionality and opportunity, but also question the viability and necessity of some human work that used to be considered intellectual yet has now become an easily automatable commodity. Education, unexpectedly, has now become burdened by an especially crucial role of charting long-range strategies for discovering viable human skills that would guarantee their place in the world of the ubiquitous use of AI in the intellectual sphere. We highlight weaknesses of the current AI and, especially, of its LLM-based core, show that root causes of LLMs' weaknesses are unfixable by the current technologies, and propose directions in the constructivist paradigm for the changes in Education that ensure long-term advantages of humans over AI tools.

Paper Structure

This paper contains 11 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Empirical Observations of the LLM's weaknesses
  3. Fundamental Foundations of the LLMs' Flaws
  4. Fundamental Linguistic Problems of LLMs
  5. Fundamental Technical Problems of Transformers
  6. Summary of What LLMs Can't Do Very Well, and Humans Better Do It Better
  7. How Education Can Foster Human Advantages Over AI
  8. Is Traditional Education Paradigm Up to the Task of Fostering Human Advantages Over AI?
  9. Transition of the Current Education Practices into the Forms Targeting Human Advantages Over AI?
  10. Methodological Approach to Implement Piaget-Vygotsky's Constructivist Education and Reanimate Humboldtian University Principles
  11. Conclusions

Figures (1)

  • Figure 1: Thought-Action concept layers: Thought-Reflection, Thought-Communication, and (thought)Action.