This paper introduces a standardized framework for measuring the Sweller Load metric — a dynamic, individualized indicator of optimal cognitive load designed to accelerate human learning. Moving beyond traditional static assessments, the framework proposes a real-time, multidimensional cognitive tensor that captures cognitive, behavioral, and emotional states during learning interactions. We outline a three-layer measurement system:(1) conversational and behavioral signals,(2) cognitive-behavioral fingerprinting, and(3) advanced cognitive and emotional profiling. The paper details the signal-fusion architecture, tensor construction, calibration protocols, and validation strategies needed to operationalize this approach in adaptive learning environments. We further address the ethical, privacy, and fairness considerations inherent in cognitive fingerprinting, and propose open datasets and tools to enable community-wide collaboration. We invite researchers, educators, and developers to join us in refining and advancing this framework, laying the groundwork for next-generation adaptive learning systems that are personalized, ethical, and responsive to each learner’s cognitive capacity.