Backpropagation learning, first formally popularized in the 1980s by David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams (1986), represents a cornerstone in both artificial intelligence and psychological theory of learning. Building upon earlier insights into neural computation by Frank Rosenblatt (1958) and the cognitive frameworks of Donald Hebb (1949), backpropagation provided a mathematically elegant way for artificial neural networks to learn from mistakes, by adjusting internal “weights” to reduce future error. Psychologically, this mirrors how humans refine behaviour through feedback and reflection, turning errors into opportunities for growth. The principle behind backpropagation, which is that of systematically transforming failure into insight, offers profound implications for personal wellbeing. It reminds us that self-correction and adaptive learning are the roots of resilience and emotional intelligence. On a societal level, this framework supports a culture of continuous improvement rather than punishment, encouraging systems, whether educational, organisational, or civic, to learn from experience. In this sense, backpropagation is not only a computational method but also a metaphor for collective psychological health and human flourishing. However effective human or artificial systems may be in ‘learning’, they may or may not have ‘comprehension’. Humans are capable of it, but despite having knowledge they may yet fail to know and understand the truth. Non-biological systems of artificial intelligence will remain incapable of comprehension. Consciousness knowledge of the truth, in both its psychological and spiritual expression belongs only to those who come into a personal relationship with God through faith in Christ, which is why the apostle Paul talks about those who are “always learning and never able to come to the knowledge of the truth” (2 Timothy 3:7).