Research on the Application of Artificial Intelligence Technology in Education and Teaching

The convergence of Artificial Intelligence (AI) and higher education is revolutionising the paradigm of traditional teaching and learning, this paper proposes a novel framework on top of previous research by means of an advanced dialogue system empowered by attentional mechanisms in the field of Artificial Intelligence in which an AI-driven dialogue agent dynamically evaluates student responses over multiple interactions,using attentional mechanisms to identify key semantic patterns, from conceptual comprehension to logical coherence.The system calibrates student responses through iterative scoring and averaging to build adaptive learner profiles that accurately categorise students into five levels at different proficiency levels (e.g., introductory, basic, intermediate, advanced, proficient).In addition to automated assessment, this approach facilitates personalised learning interventions: high-achieving students receive tailored resources for intellectual development, while students with learning difficulties receive targeted remediation through micro-lessons curated by AI. Technological innovations include a hybrid architecture combining a bi-directional GRU network with a context-aware attention layer optimised to detect subtle awareness gaps in open academic discourse.As higher education struggles to respond to the needs of a mass and diverse learner population, this research highlights how AI's dual role as an analytical tool and instructional partner transforms classrooms into responsive ecosystems where every conversation shapes tomorrow's learning trajectory.