Learnomics: A Novel Framework for Understanding and Enhancing Human Learning Through Multi-Modal Data Integration and Artificial Intelligence
The convergence of artificial intelligence, neuroscience, and data analytics has created unprecedented opportunities to understand and enhance human learning, yet the field lacks a unified framework for integrating these diverse approaches. This review introduces Learnomics, a groundbreaking interdisciplinary framework inspired by genomics, that systematically maps and analyzes the complex interplay of factors governing human learning. Just as genomics revolutionized our understanding of biological inheritance and development, Learnomics aims to transform our comprehension of learning by identifying, measuring, and interpreting the myriad variables that influence educational outcomes.
Building upon recent advances in educational neuroscience and artificial intelligence in education, Learnomics proposes to map what we term the "learning genome"—a comprehensive representation of cognitive, emotional, behavioral, and environmental factors that shape individual learning trajectories. This ambitious undertaking seeks to bridge the gap between theoretical understanding and practical application in education, leveraging cutting-edge technologies and methodologies to create more effective, personalized learning experiences.
In this review, we examine the theoretical foundations of Learnomics, exploring its methodological approaches and potential applications across various educational contexts. We introduce the Human Learnome Project, a global initiative designed to systematically explore learning processes through large-scale data collection and analysis. Furthermore, we address critical considerations regarding ethics, technology implementation, and scalability that will shape the future development of this field. Through this comprehensive analysis, we aim to demonstrate how Learnomics could fundamentally transform our approach to education and learning optimization.
