AI-Driven Paradigm Shift in Personalized Learning: Cognitive Reconfiguration and Institutional Adaptation in Higher Education

Engineering notes

The research finds that AI significantly improves learning efficiency and optimizes the allocation of teaching resources, yet it also faces multiple challenges such as data security and the transformation of teachers’ roles.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The deep integration of artificial intelligence (AI) technology with higher education is driving a fundamental transformation in personalized learning paradigms, triggering cognitive reconfiguration among learners and educators within the higher education field, as well as adaptive adjustments at the institutional level. This study comprehensively employs literature review, questionnaire surveys, and qualitative interviews to systematically analyze the current status, positive impacts, and practical challenges of AI applications in personalized learning at universities. The research finds that AI significantly improves learning efficiency and optimizes the allocation of teaching resources, yet it also faces multiple challenges such as data security and the transformation of teachers’ roles. Meanwhile, cognitive reconfiguration is manifested in learners’ restructuring of knowledge authority, learning processes, and human-machine relationships, as well as educators’ updated perceptions of teaching roles, educational technology, and talent cultivation goals. Based on these findings, promoting the paradigm shift of personalized learning requires systematic institutional adaptation in terms of technical specifications, faculty development, and resource allocation, providing theoretical support and practical pathways for AI-empowered higher education.

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