Principles of Learning
in the Age of AI

A collection of research-backed principles that educators can use to navigate AI's impact on how students learn, think, and build lasting knowledge.

12 Principles · 54 Citations

The framework presented here draws substantially on Artificial intelligence, cognitive offloading and implications for education, a 2026 research report by Jason M. Lodge and Lynn Loble published by the University of Technology Sydney. That report synthesizes recent empirical research on cognitive offloading, metacognition, and AI-mediated learning into a coherent analytical framework grounded in Cognitive Load Theory and the science of self-regulated learning. The principles below reorganize and extend that analysis into a practitioner-facing format, supplemented by additional sources from the broader research literature.

 

A note on evidence and interpretation

This resource synthesizes findings from experimental, quasi-experimental, correlational, and qualitative studies. Where the underlying research is correlational rather than experimental, the text describes reported associations and the researchers' proposed interpretive frameworks. Language such as "leads to," "produces," or "creates a cycle" reflects theoretical models proposed in the cited literature, not necessarily established causal relationships. Readers interested in the strength of evidence for any specific claim are encouraged to consult the original studies listed in the Key Studies sections.

This resource has not been peer-reviewed. It is intended as a practitioner-facing synthesis, not as original scholarship. Educators are encouraged to consult the original studies for complete findings, methodological details, and the authors' own qualifications of their results.

Based on peer-reviewed research in cognitive science, educational psychology, and instructional design.