HUMAN-CENTRIC LEARNING VS MACHINE CENTRIC LEARNING: SYNERGIES, CHALLENGES, AND FUTURE DIRECTIONS

Abstract

Human learning and machine learning (ML) represent distinct yet complementary paradigms with significant implications for education, healthcare, and occupational therapy. Human-centric learning is shaped by cognitive, social, cultural, and ethical dimensions, emphasizing meaning-making, adaptability, and transfer across contexts. In contrast, ML relies on data-driven algorithms, excelling in pattern recognition, scalability, and task-specific efficiency but lacking intentionality, transparency, and socio-cultural grounding. Comparative analysis highlights complementarities: humans contribute creativity, empathy, and ethical reasoning, while machines provide speed, consistency, and predictive power. Applications demonstrate this synergy—adaptive learning platforms in education, diagnostic tools in healthcare, and wearable or AI-based supports in occupational therapy—provided human judgment and relational practice remain central. Key challenges include algorithmic bias, opacity, privacy concerns, and the risk of diminishing human connection. Future directions emphasize explainable AI, neuro-symbolic models, ethical frameworks, and collaborative human–AI integration. Ultimately, the integration of human-centric and machine learning offers transformative opportunities, but success depends on balancing technological efficiency with human values and contextual understanding.