ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN EDUCATION: A CONCEPTUAL FRAMEWORK FOR PERSONALIZED AND ADAPTIVE LEARNING

Artificial Intelligence (AI) and Machine Learning (ML) are reshaping the landscape of higher education by enabling personalized, adaptive, and data-driven learning experiences. This paper presents a conceptual framework that integrates two core AI/ML techniques — classification and natural language processing (NLP) — into the teaching-learning process at the undergraduate level. The framework is organized around three functional layers: data acquisition, adaptive intelligence, and pedagogical delivery. Using an illustrative case drawn from a B.Sc. Computer Science classroom setting, the paper demonstrates how learning-pattern data can be used to predict at-risk students through classification and generate remedial practice content and formative feedback through NLP. The paper further discusses implementation challenges specific to resource-constrained institutions, including data infrastructure, faculty readiness, and ethical considerations around student data privacy. The proposed framework offers a practical starting point for institutions seeking to adopt AI-enabled pedagogy without large-scale technological investment.