Design and Development of a Rule-Based AI Tutoring System for Remediating Misconception in Secondary School Biology

This study explores the development of a rule-based AI tutoring system named Ecology Perfect Master (EPM) designed to address misconceptions in secondary school biology. Utilizing the first three phases of the Design and Development Research methodology—analysis, design, and development—the study begins by identifying common misconceptions in ecological concepts and gathering requirements from educators and students. The design phase translates these requirements into a comprehensive plan, featuring an intuitive user interface, a robust knowledge base, and an efficient inference engine capable of diagnosing and remediating misconceptions through tailored feedback. The development phase brings the design to fruition with a knowledge base populated by expert-validated content, interactive elements for enhanced learning experiences, and iterative testing to refine the system’s functionality and usability. Preliminary evaluations indicate that EPM effectively identifies and addresses misconceptions, enhanced students' understanding of complex biological concepts. The methodology and procedures outlined in this research offer a replicable framework for developing similar AI tutoring systems across various subjects and educational levels