Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments

The need for intelligent, transparent, and adaptive personalized instruction systems has increased due to the quick diversification of higher education settings and the exponential expansion of educational big data. In order to address the pedagogical, cultural, and cognitive variability present in multicultural higher education settings, this study suggests a Big Data-Driven Personalized Instruction framework powered by Explainable Agentic Artificial Intelligence (X-AI). The suggested approach uses autonomous agentic AI architectures that can orchestrate goal-directed learning, dynamic learner profiling, and real-time instructional adaption by utilizing large-scale, multimodal educational data, namely the HarvardX-MITx Person-Course Dataset. Explainability mechanisms are incorporated at both the model and decision levels to guarantee pedagogical trustworthiness and ethical deployment. This allows for interpretable insights into learner performance projections, instructional recommendations, and adaptive intervention tactics. To assist teachers in comprehending cross-cultural learning patterns and reducing algorithmic bias, the system incorporates feature attribution, causal inference, and visual analytics. When compared to traditional data-driven personalization techniques, the suggested methodology dramatically increases learning outcome prediction accuracy $F1\hspace{0.17em}=\hspace{0.17em}0.88$, instructional relevance, and student engagement, according to experimental evaluations carried out on diverse higher education datasets. This work advances a scalable and culturally responsive paradigm for personalized education by combining big data analytics, agentic autonomy, and explainable AI. It provides educators, institutions, and policymakers looking to transform higher education systems in an equitable, transparent, and data-driven manner with practical insights.