Artificial Intelligence (AI) is increasingly redefining educational paradigms through adaptive learning, intelligent assessment, and data-driven decision-making. This study conceptualizes AI as an extension of human intelligence and examines its integration via learner, teaching, and knowledge models to enable personalized and efficient learning environments. A hybrid teaching framework employing Artificial Neural Networks (ANN) is proposed for student classification and performance prediction, demonstrating accuracy up to 97.8% with extended data inputs. The findings highlight AI’s role in enhancing engagement, accessibility, and administrative efficiency. However, ethical concerns, particularly data privacy and over-reliance on automation, remain critical. The study emphasizes a balanced human–AI synergy to ensure effective, inclusive, and ethically grounded education systems.
Shrirame et al. (Thu,) studied this question.
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