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AI Research
arXiv CS.AI·May 25·8 min read
Agentivism: a learning theory for the age of artificial intelligence
TL;DR
Existing learning theories cannot explain when AI-assisted task completion represents genuine human learning versus mere performance delegation. Agentivism proposes a new learning framework defining learning as durable capability growth through selective AI delegation, verification of AI outputs, internalization of results, and transfer under reduced support.
✦ Why It Matters
Engineers building AI-assisted learning systems can use Agentivism to design features ensuring users develop genuine capability rather than surface-level task completion.
Key Takeaways
- Implement selective delegation of tasks to AI for effective learning.
- Encourage critical assessment of AI-generated outputs among learners.
- Promote active engagement with AI tools to enhance understanding.
- Foster durable human capabilities despite reliance on AI assistance.
How It Works
Agentivism operates by encouraging learners to selectively delegate tasks to AI while maintaining oversight of AI contributions. This involves epistemic monitoring, where learners critically evaluate the information provided by AI, and reconstructive internalization, which requires them to actively engage with and internalize AI-generated outputs.
By doing so, learners can develop a deeper understanding and retain the ability to transfer knowledge to new contexts.