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Unveiling Pathways and Barriers: How AIGC-Driven Personalized Learning Shapes Sustainable Educational Development—Empirical Evidence from Chinese Higher Education

por Zhileng Xiong y Qing Huang

Publicada: 7 de agosto de 2025
Servidor: Preprints.org
DOI: 10.20944/preprints202502.0194.v5

The rapid advancements in Artificial Intelligence-Generated Content (AIGC) technol-ogy have positioned AIGC-driven Personalized learning as a critical pathway for ad-vancing educational sustainability, particularly in addressing inclusiveness, equity, and quality. This study examines the mechanisms and challenges of AIGC applications in Chinese higher education through a mixed-methods approach combining systematic literature review and empirical analysis. Leveraging the SWOT framework and Ana-lytic Hierarchy Process (AHP) with 928 valid student questionnaires, we establish a multi-criteria decision-making framework to evaluate strategic priorities and opera-tional risks.

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