AI-Based Knowledge Discovery Models in Education

Anthony Cronin

AI-Based Knowledge Discovery Models in Education

Author(s)	Dr. Anthony Cronin
Country	Ireland
Abstract	The increasing digitalization of education has generated extensive volumes of academic, learner, instructional, and institutional information, creating a growing need for intelligent approaches to identify meaningful knowledge from complex educational data. Artificial intelligence-based knowledge discovery models provide computational mechanisms for extracting patterns, relationships, trends, and actionable insights from structured and unstructured educational information. This study examines AI-based knowledge discovery models in education through a conceptual and literature-based research design. The analysis focuses on machine learning, natural language processing, educational data mining, learning analytics, knowledge graphs, recommendation systems, predictive modeling, and generative artificial intelligence as mechanisms for educational knowledge discovery. An integrated framework is proposed connecting educational data sources, preprocessing, AI-based knowledge extraction, pattern identification, knowledge representation, interpretation, pedagogical application, and continuous evaluation. AI-based discovery can assist institutions and educators in identifying learner behavior patterns, understanding academic performance, discovering relationships among educational variables, organizing learning resources, supporting personalized instruction, and improving evidence-informed decision-making. Nevertheless, knowledge discovery systems face challenges involving data quality, privacy, algorithmic bias, interpretability, interoperability, teacher preparedness, computational requirements, and inappropriate reliance on automated findings. Educational knowledge cannot be reduced entirely to computational patterns because learning is influenced by social, cultural, psychological, instructional, and contextual factors. The study emphasizes that AI-based knowledge discovery should operate as a decision-support mechanism that complements educators and institutional stakeholders rather than replacing professional judgment. Future research should investigate explainable educational knowledge discovery, multimodal learning data integration, privacy-preserving analytics, intelligent knowledge graphs, causal discovery, adaptive educational recommendation systems, and human-centered AI governance. The study provides a conceptual foundation for applying AI-based knowledge discovery models to educational research, teaching, learning management, and institutional improvement.
Keywords	Artificial Intelligence, Knowledge Discovery, Education, Educational Data Mining, Learning Analytics, Machine Learning, Knowledge Graphs, Natural Language Processing, Educational Data, Intelligent Education
Field	Engineering
Published In	Volume 6, Issue 5, September-October 2024
Published On	2024-09-09

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American International Journal of Advanced Research and Development

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AI-Based Knowledge Discovery Models in Education

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