Designing an AI-Driven Smart Learning Environment for Personalised Instruction

Abstract

Regular education commonly falls below the mark in fulfilling students' varied needs because it depends on the standardized style with little scope for adaptability and individuality. Recent developments in artificial intelligence have benefited the formulation of learning spaces with the potential to adapt students' needs dynamically. This paper suggests a theoretical model of an AI-enabled smart learning environment with personalised instruction at its core. The platform combines learner modelling, learning analytics, and generative artificial intelligence in order to provide adaptive content, feedback, and testing. Notably, the incorporation of teacher-in-the-loop oversight allows for accountability and alignment with learning goals. The system architecture is organised in three layers: data governance, intelligence, and user interface. Teachers can view students' performance through dashboards while learners’ complete adaptive activities. Learner models that monitor acquired knowledge, level of engagement, and misconceptions allow the generative AI engine to generate explanations and exercises in real-time. Data analytics offer the steady streams of feedback loops improving customisation and facilitating evidenced-based pedagogic decisions. Governance modules raise ethical and privacy concerns with transparent and secure handling of the data. The viability in tracing students' behavioural activity and producing personalised feedback is illustrated with a prototype designed with Python and AI functions. The research indicates the potential in such learning spaces in fostering learning equity, lowering instructional expenditure, and enhancing students' involvement. Recommendations are made for researchers, teachers, and policy-makers in order to direct future research and practical application.