This paper critically examines AI-driven learning personalization, identifying seven interconnected problems: technological determinism, widening educational disparities, privacy concerns, algorithmic bias, neglect of human relationships, trivialization of learning, and commercialization. These challenges represent fundamental issues affecting educational values and social equity rather than mere technical difficulties. Adopting a sociotechnical systems perspective, the study reveals that current AI-driven personalization often reflects commercial priorities over pedagogical expertise, resulting in pseudo-personalization that undermines authentic learning. The research proposes a hybrid educational model that leverages AI for basic skill acquisition while preserving human-centered aspects including creative thinking, critical analysis, and emotional support. Key recommendations include reaffirming public education values, promoting open-source educational technology, establishing educational data sovereignty, and redefining educator roles. The findings emphasize that genuine learning individualization cannot be resolved through technological solutions alone. The future of education depends on careful integration guided by educational philosophy and value judgments that prioritize human development over market-driven metrics, requiring educators to critically evaluate technology rather than accepting commercial solutions uncritically.
Shigenori Tanaka (Wed,) studied this question.