Utilising an Interactive Deep Learning Framework to Enhance the Effectiveness of Intelligent Teaching Practices

Abstract

Conventional teaching methodologies often fail to accommodate the diverse needs and learning styles of individual students, presenting a persistent challenge in education. This study proposes an innovative interactive deep learning framework that transforms intelligent teaching practices by integrating recurrent neural networks (RNNs). By leveraging the temporal modelling capabilities of RNNs within an interactive instructional environment, the framework dynamically analyses sequential patterns in student learning and engagement. A key contribution of this research is the development of a dynamic approach that utilises RNNs to model long-term dependencies and temporal dynamics within educational processes. This enables intelligent teaching systems to adapt in real time to students' behavioural patterns and evolving learning trajectories. Additionally, the incorporation of real-time feedback mechanisms allows educators to intervene and refine instructional strategies based on predictive insights generated by RNNs. This iterative and interactive process fosters a highly personalised learning experience, enhancing student engagement and knowledge retention. Empirical evaluations in real-world educational settings confirm the framework’s efficacy, demonstrating substantial improvements in teaching effectiveness and student learning outcomes. This study advances the development of adaptive and responsive intelligent teaching systems capable of delivering personalised instruction on a large scale. It makes a significant contribution to educational technology by introducing a transformative interactive deep learning framework enhanced by RNNs, ad-dressing the critical issue of personalisation in education while providing a scalable solution to improve intelligent teaching methodologies and student learning experiences.