A Critical Analysis of Neurosymbolic Ai Models for Adaptive Multimodal Knowledge Integration in Conversational Chatbots for Inclusive Education

Abstract

Conversational chatbots hold significant potential for inclusive education by enabling accessible, personalized communication for diverse learners in resource-constrained environments. However, existing approaches often lack interpretability and efficiency for real-time adaptation to varied learner needs. This survey examines neurosymbolic AI approaches that integrate neural processing with symbolic reasoning to support adaptive multimodal knowledge integration in conversational chatbots for inclusive education. By analyzing recent studies (2020–2025) from IEEE, Scopus, and arXiv, evaluate the efficiency, interpretability, and adaptability of these approaches for diverse educational contexts. This work identifies critical gaps and proposes a novel framework to guide future research, offering a foundation for scalable, equitable AI solutions in inclusive education.