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A system dynamics model examining how AI-assisted tools drive cognitive offloading and influence long-term human capability formation.
This project explores the long-term cognitive consequences of AI-assisted tools through a system dynamics lens. Usinga model built on System Dynamics tool, Stella, we can see how AI assisted reading can contribute to cognitive offloading.
The goal is not to make predictions, but to understand structural trade-offs and feedback mechanisms.
How does sustained AI-assisted cognitive offloading affect:
- Deep reading capability
- Skill atrophy
- Reflection effort
- Long-term human capability formation?
The model is built using system dynamics (stocks, flows, and feedback loops) and includes:
- Deep Reading Capability: accumulated human cognitive skill
- AI Assisted Reading Usage: level of reliance on AI tools
- Effortful Reading Practice (inflow to capability)
- Skill Atrophy (outflow from capability)
- Increased AI usage reduces reflection effort
- Reduced reflection lowers effortful practice
- Lower practice accelerates long-term skill decay
This creates a balancing feedback loop that trades short-term efficiency for long-term capability.
ai_cognitive_offloadingmodel.stmx - https://exchange.iseesystems.com/models/player/haritha-haridas/ai-reading-comprehension--system-dynamics