AI to Learn (AI2L): Guidelines and Practice for Human-Centered AI Utilization as a Learning Support Tool—Four Pillars of Black-Box Elimination, Accountability, Information Protection, and Energy Efficiency

Abstract

Contemporary generative AI—especially large language models (LLMs)—is rapidly permeating diverse domains such as research, education, and healthcare owing to its remarkable efficiency and expressive power. Conversely, AI systems bring serious challenges: their black-box nature, the risk of privacy leakage from input data, ethical concerns arising from outputs whose rationale is opaque, and the substantial energy consumption and environmental burden associated with large-scale deployment. This paper proposes AI to Learn (AI2L), a set of guidelines that deliberately limits AI to a learning-support role for humans and eliminates any black-box components from the final deliverables. AI2L rests on four principles: (1) humans retain ultimate decisionmaking authority; (2) human verification ensures accountability for AI outputs; (3) the risk of information leakage is rigorously minimized; and (4) AI usage is managed for energy efficiency and long-term sustainability. We examine several concrete implementations of AI2L—including Grad CAM–based image interpretation, the discovery of novel insights via symbolic regression, the development of AI-generated yet humanauditable code, and reversible anonymization for data protection—and analyze them from both practical and theoretical perspectives. Recent studies showing that foundation models fail to grasp underlying physical laws, despite high predictive accuracy, further underscore the necessity of AI2L’s approach. By acknowledging AI’s limitations and hazards while harnessing its strengths, AI2L provides a robust framework for ethical, sustainable, and human-centered integration of AI into society.