Conversational AI as an Intelligent Tutor: A Review of Dialogue-Based Learning Systems
Herbert Wanga1*
Abstract
Conversational Artificial Intelligence (AI) is rapidly transforming educational technology, primarily through the advancement of Intelligent Tutoring Systems (ITS). By leveraging natural language processing (NLP), adaptive learning algorithms, and evidence-based pedagogical strategies, these systems simulate human-like tutoring dialogues to deliver personalized, scalable, and engaging learning experiences. This article synthesizes recent literature to explore the architectural foundations, pedagogical benefits, and empirical effectiveness of conversational ITS across diverse domains. This study examines pivotal systems, including AutoTutor, Oscar CITS, and multi-agent tutors, highlighting their capabilities in modeling learner knowledge, supporting self-regulated learning, and improving educational outcomes. The discussion also addresses significant challenges related to cultural adaptability, affective computing, ethical implications, and system accessibility. Finally, the study proposes future research directions aimed at optimizing the integration of conversational AI in education. The accumulated evidence indicates that conversational AI can significantly enhance learning when designed with integrated pedagogical, affective, and ethical sensitivity.
Keywords:
conversational AI; intelligent tutoring systems; personalized learning; natural language processing; adaptive learning; educational technology; affective computing
