Grounding and Operationalising the SYNAPSE Model: Design Principles and Observable Indicators for Human‐Centered, AI‐Augmented Learning

ABSTRACT

This article consolidates and operationalises SYNAPSE, a human‐centered model that structures learning in four interrelated phases—Sensory Input, Network Adaptation, Participation, and Storage & Embodiment—for education in the age of artificial intelligence (AI). Extending an earlier conference presentation of the model, the article grounds SYNAPSE in a systematic review of 162 scientific sources, positions it against established instructional and self‐regulation frameworks, and translates it into phase‐specific design principles and observable indicators for AI‐augmented learning. Findings from a new exploratory qualitative study with teacher‐education students ( N  = 8), who used bounded generative AI within SYNAPSE‐informed lesson sequences, suggest that AI can support reflective engagement and metacognitive self‐regulation when its use is aligned with human learning phases, and underscore the fundamental role of the educator who designs the teaching sequence. The article offers implications for educators, researchers, and developers seeking AI‐mediated learning environments that promote motivation, learner autonomy, and durable knowledge construction.