arXiv:2606.12441v1 Announce Type: cross Abstract: The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as generative artificial intelligence (AI) proliferates in educational settings. These frameworks were formulated before the emergence of AI systems capable of generating, synthesizing, and reasoning about knowledge. This article critically examines each learning theory and identifies assumptions challenged by generative AI's affordances. Drawing on research in distributed cognition, extended mind, human-AI c
The rapid proliferation and capabilities of generative AI necessitate a re-evaluation of foundational learning theories previously formulated without considering such advanced AI systems.
Understanding how generative AI challenges and integrates with learning theories is crucial for adapting educational paradigms, workforce training, and human-AI collaboration effectively.
Traditional learning theories face conceptual limitations, requiring new frameworks that acknowledge generative AI's ability to create, synthesize, and reason with knowledge.
- · AI developers
- · Educational researchers
- · Lifelong learners
- · EdTech companies
- · Outdated educational institutions
- · Traditional pedagogy advocates
Generative AI systems will play a more integrated role in educational content creation and learning processes.
The definition of 'knowledge' and 'learning' will evolve to incorporate human-AI co-creation and distributed cognition.
Future workforce skills will heavily emphasize human-AI collaboration, critical evaluation of AI-generated content, and continuous adaptation to new AI capabilities.
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Read at arXiv cs.AI