An Intelligent Tutoring System Proposal Based on Chatbot and Learning Styles to the Project Management Study

This research full paper proposes an intelligent tutoring system (ITS) to support the study of software project management, considering the students' learning styles. The learning process is a complex and multifaceted phenomenon that varies significantly from one person to another. Everyone absorbs knowledge differently because each has learning characteristics that directly impact the learning process. Ignoring these differences can result in inefficient and demotivating teaching. Therefore, teachers must invest time in understanding each student's learning style, strengths, and weaknesses, which can be overwhelming, especially in classrooms with a high student-teacher ratio. In this scenario, AI-based technology can be a strong ally. For example, chatbot technology offers an opportunity to adapt to these individual differences and to enable a more engaging and friendly approach for students, contributing to an active and interactive learning environment when exercising the role of a study companion and, why not, a bit of study guide. In this context, this paper proposes developing and applying a chatbot-based ITS called Juh to identify each student's learning style and, based on this understanding, present study activities appropriate to their learning style. The ITS uses meaningful learning theory to personalize the learning journey, detecting five profiles: active, constructive, intentional, authentic, and cooperative. This study chose project management (PM) as its knowledge area, particularly project management methodologies, aiming to evaluate the benefits of this research proposal. In this context, this research proposes to answer the following research question: RQ) How can the study of project management be supported actively and interactively, considering the students' learning styles? As its main contribution, the solution aims to offer support in the introduction to the study of project management and improve the learning experience according to the individual characteristics of each student, maximizing the assimilation of knowledge. Therefore, the difference lies in exploring how and what to learn. The solution was developed using the Design Science Research method and the low code Blip platform. After prototyping and evaluating the ITS with PM and education experts, promising points about the approach were observed, with users highlighting improvements in productivity and ease of use of the tutoring system.