Learning in the Age of Artificial Intelligence: Reimagining Education Through Classical Learning Theories

Natalia Galaburda 

ORCID iD: 0009-0000-4432-6158 

University of the People (USA)

Introduction: A New Paradigm or Well-Forgotten Old Wisdom?

When ChatGPT first appeared in university classrooms, many instructors experienced panic: "How can we teach now if AI can write any essay?" However, a deeper analysis reveals that artificial intelligence does not contradict the fundamental principles of learning established by Dale Schunk, John Dewey, and other classics of pedagogical science. On the contrary, it makes these principles more relevant and requires their creative reinterpretation.

According to Schunk's (2012) definition, "learning is an enduring change in behavior or in the capacity to behave in a given fashion, which results from practice or other forms of experience" (p. 3). The keyword here is "enduring." AI can generate answers, but can it create enduring changes in human understanding? This question lies at the heart of the 21st-century educational revolution.

Constructivist Foundation of AI Education

From Piaget to Personalized AI

Jean Piaget showed that children do not simply receive knowledge but actively construct it through interaction with their environment. Modern AI systems, such as adaptive learning platforms, embody these principles at a new level. When an AI tutor analyzes a student's error patterns and offers personalized exercises, it does not replace constructivist learning—it enhances it.

Example from practice: Khan Academy uses AI to create individual learning trajectories. If a student struggles with algebraic equations, the system does not simply provide the correct answer but offers a series of scaffolded exercises that help build understanding step-by-step. This is a direct implementation of Piaget's idea that knowledge is built gradually through active engagement.

Zone of Proximal Development in the Digital Age

Lev Vygotsky conceptualized learning as a social process occurring in the "Zone of Proximal Development" (ZPD)—the space between what a student can do independently and what they can do with assistance. AI assistants become a new type of "more knowledgeable other," but with unique capabilities:

• 24/7 availability - AI never gets tired or irritated
• Infinite patience - can explain concepts as many times as needed
• Multimodality - can be explained through text, images, videos, and simulations
• Adaptivity - adjusts to each student's learning style

But there is a critical limitation: AI can be a tool within the ZPD, but cannot replace the human relationships that Vygotsky considered the foundation of learning.

Culturally Responsive Teaching and AI

Digital Equity as a New Challenge

Geneva Gay (2018) emphasizes the importance of integrating students' cultural characteristics into the learning process. AI creates new opportunities for culturally responsive education, but also new risks of cultural hegemony.

Opportunities:

• Multilingual AI systems can support learning in students' native languages
• Culturally adaptive content - AI can generate examples relevant to different cultural contexts
• Overcoming language barriers - real-time translation and cultural adaptation

Risks:

• Algorithmic bias - AI systems may reproduce existing inequalities
• Cultural homogenization - dominance of Western educational models in AI
• Digital divide - unequal access to technology amplifies educational gaps

Case Study: AI in Indigenous Education

In New Zealand, an AI system is being developed for learning the Māori language that does not simply translate words but understands cultural context. The system recognizes that in Māori culture, some knowledge is transmitted only to certain people at certain times. This is an example of how AI can serve cultural preservation rather than replacement.

Critical Pedagogy in the Age of Algorithms

Freire Meets Machine Learning

Paulo Freire (1970) warned about the "banking model" of education, where students become passive recipients of information. AI creates the risk of strengthening this model—students may become even more passive, relying on algorithms for thinking.

But AI can also liberate education:

• Automation of routine frees time for critical thinking
• Access to information democratizes knowledge
• Personalization allows everyone to learn at their own pace

Key question: How to use AI to develop critical consciousness rather than suppress it?

Algorithmic Literacy as a New Critical Competency

Students must understand:

• How algorithms that shape their educational experience work
• What biases may be built into AI systems
• How to critically evaluate AI-generated information
• When to trust AI and when to rely on human judgment

Best Global Educational Systems and AI

Finland: AI in Service of Well-being

The Finnish education system, known for its emphasis on children's well-being and absence of standardized testing, integrates AI cautiously and thoughtfully:

• AI assistants help teachers identify students needing additional support
• Adaptive systems support inclusive education
• Ethical frameworks for AI use are developed jointly by teachers, parents, and students

Principle: Technology serves pedagogy, not vice versa.

Singapore: AI for Future Skills

The Singaporean system, known for high academic standards, uses AI to prepare for the future:

• Predictive analytics helps identify learning gaps before they become critical
• AI simulations for studying complex systems (economics, ecology, engineering)
• Automated assessment frees teachers for more creative work

Principle: AI as a tool for developing higher-order thinking.

Estonia: Digital Citizenship from Birth

Estonia, the first country where programming became a mandatory subject, creates a model of an "AI-literate" society:

• Computational thinking from elementary school
• AI ethics as part of humanities education
• Students as creators of AI solutions, not just consumers

Principle: Democratizing AI through education.

Reimagining Classical Learning Theories

Behaviorism in the AI Era: New Life for Old Ideas

Although behaviorism is criticized for being mechanistic, some of its principles gain new relevance in AI education:

• Immediate feedback - AI can provide instant and accurate feedback
• Adaptive reinforcement - AI can personalize reward systems
• Micro-learning - breaking complex skills into small, measurable components

But with important differences: Modern "behaviorism" accounts for internal processes and individual differences.

Cognitive Theory and AI: Understanding Thinking

Cognitive learning theory finds new application in understanding how people interact with AI:

• Cognitive load - how to design AI interfaces that don't overload working memory
• Metacognition - developing students' ability to reflect on their own thinking in the context of AI
• Transfer learning - how knowledge gained with AI assistance transfers to new contexts

Social Learning Theory: AI as a New Social Actor

Albert Bandura showed the importance of observational learning and social modeling. AI characters and virtual mentors become new role models:

• Virtual mentors can demonstrate problem-solving processes
• AI peers can model collaboration and group work
• Adaptive personalities of AI can adjust to cultural and individual preferences

Practical Recommendations for Educational Systems

For Teachers:

1. Become AI-literate, but not AI-dependent
2. Use AI to automate routine, freeing time for human interaction
3. Develop skills that AI cannot replace: empathy, creativity, critical thinking
4. Teach students to collaborate with AI, not compete against it

For Educational Systems:

1. Invest in teacher training on working with AI
2. Develop ethical frameworks for AI use in education
3. Ensure equal access to AI technologies
4. Support research on AI's impact on learning

For Educational AI Developers:

1. Include educators in the development process from the beginning
2. Test on diverse groups of students and contexts
3. Ensure transparency of algorithms for users
4. Develop with cultural sensitivity in mind

Conclusion: The Human in the Digital

The integration of AI into education does not mean replacing the human element—it means enhancing it. The classical learning theories of Schunk, Piaget, Vygotsky, Dewey, and others have not become obsolete in the AI era. On the contrary, they provide the necessary foundation for understanding how to use AI for truly human education.

Key Principles of Education in the AI Era:

1. Learning remains a social process - AI can support but not replace human relationships
2. Cultural responsiveness is critically important - AI must serve diversity, not homogenization
3. Critical thinking is more important than ever - students must be able to evaluate AI-generated information
4. Equity requires active effort - AI can amplify inequality without conscious resistance
5. Teachers remain irreplaceable - but their role evolves from information transmitters to learning facilitators

The best educational systems of the future will be those that can harmoniously integrate the power of AI with the wisdom of human pedagogy. They will use technology to create more personalized, accessible, and equitable education while remaining deeply human in their essence.

As Dewey would say, education is not preparation for life but life itself. In the AI era, this truth becomes even more relevant: we learn not to compete with machines, but to remain fully human in a world where the boundaries between human and artificial intelligence are becoming increasingly blurred.

References

Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience, and school. National Academy Press.

Dewey, J. (1897). My pedagogic creed. The School Journal, 54(3), 77-80.

Freire, P. (1970). Pedagogy of the oppressed. Continuum International Publishing Group.

Gay, G. (2018). Culturally responsive teaching: Theory, research, and practice (3rd ed.). Teachers College Press.

Piaget, J. (1952). The origins of intelligence in children. International Universities Press.

Schunk, D. H. (2012). Learning theories: An educational perspective (6th ed.). Pearson.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.