Extending Cognitive Load Theory: The CLAM Framework for Biometric, Adaptive, and Ethical Learning

Cognitive Load Theory (CLT) and the Cognitive Theory of Multimedia Learning (CTML) have long served as foundational frameworks in instructional design. However, their applicability to contemporary, technologically mediated learning environments remains under-theorized. This review critically examines CLT and CTML, focusing on their assumptions, empirical contributions, and current limitations in addressing the complexities of dynamic, AI-enhanced educational settings. The discussion is further enriched through engagement with complementary perspectives, including self-regulated learning, dual process theory, and connectivism. These frameworks illuminate conceptual convergences but also expose theoretical tensions, particularly regarding unresolved constructs such as germane cognitive load and the methodological challenges associated with real-time cognitive load measurement. In response to these gaps, this paper proposes the Cognitive Load-Aware Modulation (CLAM) strategy—a conceptual model designed to extend cognitive load principles in adaptive, ethically responsive learning environments. Synthesizing insights from cognitive psychology, educational technology, and affective computing, CLAM supports the design of personalized, data-driven instructional systems attuned to learners’ cognitive and emotional states. The model emerges not merely as a theoretical contribution, but as a future-oriented framework rooted in the critical synthesis of the reviewed literature. Its practical applications for real-world educational settings are outlined, and its empirical validation constitutes the next phase of our ongoing research project.