Learning from Long-Term Engagement: Adaptive Tutoring Dialogue Planning for Personalized Education

You have witnessed how technoloy like artificial intelligence (AI) developed and has changed the entire paradigm of learning. Nevertheless, current state-of-the-art adaptive learning systems strive to overcome common shortcomings suffered by existing systems, including reliance on a theoretical perspective, repetitive patterns in student modelling, over-dependence on synthetic data, high learning curve and low investigation into their long-term performance.This research develops an adaptive learning system that integrates adaptive learning system with educational practice and pedagogy, considering multiple factors including scalability, bias mitigation and costeffectiveness to enhance student engagement and long-term transfer of knowledge. A first in this domain the current study will incorporate real world student data to create personalized learning paths mindful of data privacy and ethical AI deployment by utilizing advanced security mechanisms such as block chain and federated learning unlike earlier studies. We will put forth a crossdisciplinary framework for teaching through multimodal AI techniques (e.g., STEM, humanities, and creative disciplines). Moreover, lightweight AI models will be developed for deployment in resource-limited educational institutions to ensure accessibility. Through filling up these key gaps, such work acts as a step towards the creating of a more fair, transparent, and scalable adaptive learning that can be widely implemented across the globethus, establishing new benchmarks for the future of education powered by AI.