EduAssist: A Hierarchical Conversational Educational Recommendation (HCER) Framework for Personalized and Adaptive Learning

Abstract: The growth of digital learning platforms has changed the way students look for knowledge and academic support. As these environments continue to expand, there is a rising demand for tools that can respond instantly and adapt to the needs of individual learners. Educational chatbots have emerged as one answer to this demand, offering students immediate answers, access to study resources, and academic guidance outside the traditional classroom. This paper presents EduAssist, a conceptual educational chatbot built around Natural Language Processing (NLP), designed to support personalized learning and sustained student engagement. By analyzing user queries through NLP techniques, the system is intended to generate relevant answers and recommend educational resources tailored to each learner's needs. Through the combination of conversational interaction, structured knowledge management, and voice-based accessibility, EduAssist aims to make independent study more approachable and consistent. Because this work presents a system design rather than a deployed application, the paper focuses on architecture, proposed functionality, and anticipated behavior, and identifies the steps — including prototype development and empirical evaluation — required to validate the framework in practice.

Keywords: Educational Chatbot, Natural Language Processing (NLP), Personalized Learning, Adaptive Learning, Conversational AI, Student Support Systems, Educational Technology, Intelligent Tutoring Systems, Learning Analytics, Artificial Intelligence.