Designing a Model for an AI-Based Intelligent Assistant for Personalized Learning in Higher Education

Abstract

In recent years, remarkable advancements in artificial intelligence technology have created new opportunities for transforming educational systems and enhancing student learning. This study focuses on designing a model for an AI-based intelligent assistant to provide a personalized learning experience in higher education. A qualitative approach and grounded theory strategy were used to conduct the research. The study population included all experts, professors, and senior managers in the fields of educational sciences and software engineering in higher education, with 21 individuals selected through purposive sampling and theoretical strategy for interviews. Semi-structured interviews were employed as the research tool. For qualitative data analysis, open, axial, and selective coding methods were utilized. The study results indicated that data analysis, artificial intelligence and adaptive learning, machine learning and modeling, assessment and feedback systems, support and counseling, educational technologies, and infrastructure were considered strategies for designing an AI-based intelligent assistant for personalized learning in higher education. It can be concluded that through the use of machine learning algorithms and natural language processing, the intelligent assistant will be capable of providing effective educational recommendations by collecting data on learners' performance, behavior, and preferences. The study also examines potential challenges and barriers in implementing such a system in educational environments and ultimately provides solutions to realize this model. The ultimate aim of this research is to improve learning quality and facilitate access to education using modern technologies.