Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
Summary
This position paper argues that incorporating metacognition as a design principle can lead to more accurate, secure, and efficient AI systems, and demonstrates the concept through a Federated Learning case study and a software framework for experimentation.
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# Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI Source: [https://arxiv.org/abs/2605.15567](https://arxiv.org/abs/2605.15567) [View PDF](https://arxiv.org/pdf/2605.15567) > Abstract:This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI\. The metacognitive solution involves systems monitoring their own states and judiciously allocating resources depending on each problem instance's difficulty or cost of mistakes\. Drawing inspiration both from past work on resource\-rational AI and from well\-documented metacognitive strategies in psychology and cognitive science, we identify specific challenges in embedding these strategies into AI design and highlight open theoretical and implementation problems\. We showcase these principles through a tangible example of improved learning efficiency, effectiveness, and security in a Federated Learning \(FL\) case study\. We show how these principles can be translated into practice with a novel software framework developed specifically to allow the community to design, deploy, and experiment with metacognition\-enabled AI applications\. ## Submission history From: Sergei Chuprov \[[view email](https://arxiv.org/show-email/8f70ea81/2605.15567)\] **\[v1\]**Fri, 15 May 2026 03:17:02 UTC \(868 KB\)
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