English

Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence

Neurons and Cognition 2024-01-03 v1 Artificial Intelligence

Abstract

This paper offers a novel conceptual framework comprising four essential cognitive mechanisms that operate concurrently and collaboratively to enable metalearning (knowledge and regulation of learning) strategy implementation in young children. A roadmap incorporating the core mechanisms and the associated strategies is presented as an explanation of the developing brain's remarkable cross-context learning competence. The tetrad of fundamental complementary processes is chosen to collectively represent the bare-bones metalearning architecture that can be extended to artificial intelligence (AI) systems emulating brain-like learning and problem-solving skills. Utilizing the metalearning-enabled young mind as a model for brain-inspired computing, this work further discusses important implications for morally grounded AI.

Keywords

Cite

@article{arxiv.2401.01001,
  title  = {Metalearning-Informed Competence in Children: Implications for Responsible Brain-Inspired Artificial Intelligence},
  author = {Chaitanya Singh},
  journal= {arXiv preprint arXiv:2401.01001},
  year   = {2024}
}

Comments

27 pages, 3 figures

R2 v1 2026-06-28T14:06:30.992Z