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Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments
Conglin Qiu1, Kunkun Cui2, Zilong Wang2
1Korea Dongshin University, Dongshin University, Naju-si, Korea.
Big Data
|June 29, 2026
Summary
This study introduces a Big Data-Driven Personalized Instruction framework using Explainable Agentic Artificial Intelligence (X-AI) to enhance multicultural higher education. The X-AI approach improves learning outcomes and student engagement through adaptive, transparent, and culturally responsive educational strategies.
Area of Science:
- Educational Technology
- Artificial Intelligence in Education
- Big Data Analytics
Background:
- Increasing diversity in higher education and the growth of educational big data necessitate intelligent personalized instruction systems.
- Existing systems struggle with pedagogical, cultural, and cognitive variability in multicultural settings.
Purpose of the Study:
- To propose a Big Data-Driven Personalized Instruction framework powered by Explainable Agentic Artificial Intelligence (X-AI).
- To address variability in multicultural higher education settings through adaptive, transparent, and culturally responsive learning.
- To ensure pedagogical trustworthiness and ethical deployment via explainability mechanisms.
Main Methods:
- Utilizing autonomous agentic AI architectures for goal-directed learning, dynamic learner profiling, and real-time instructional adaptation.
- Leveraging large-scale, multimodal educational data (HarvardX-MITx Person-Course Dataset).
- Incorporating explainability mechanisms (feature attribution, causal inference, visual analytics) for transparency and bias reduction.
Main Results:
- The X-AI framework significantly improves learning outcome prediction accuracy (F1 = 0.88) compared to traditional methods.
- Demonstrates enhanced instructional relevance and increased student engagement in diverse higher education datasets.
- Provides interpretable insights into learner performance, instructional recommendations, and adaptive interventions.
Conclusions:
- The study advances a scalable, culturally responsive paradigm for personalized education by integrating big data, agentic autonomy, and explainable AI.
- Offers practical insights for educators, institutions, and policymakers aiming for equitable, transparent, data-driven higher education transformation.
- Highlights the potential of X-AI to navigate complexities of multicultural learning environments effectively.
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