In the Philippines, elementary students face a significant educational hurdle, particularly in Grade 4, where foundational competencies prove challenging to grasp. This research aims to provide a possible solution for this issue by developing and investigating the functionality of an electronic GLM-powered learner-oriented tool (EGLOT), designed to act as an educational companion that leverages a generative language model to personalize the learning experience for Grade 3 students in mathematics. EGLOT assesses students' competencies and delivers tailored learning modules based on their results. EGLOT integrates hardware, utilizing ESP32 for speech-to-text and text-to speech capabilities, along with software functionalities accessible through a teacher dashboard for monitoring and control. Through rigorous statistical analyses, including ISO 25059 and Word Error Rate (WER) assessments, EGLOT's accuracy and reliability were validated. Achieving a 91.94% accuracy rate in speech-to-text transcription, EGLOT demonstrates its ability to identify individual student weaknesses and design personalized learning modules, thereby empowering teachers with targeted interventions. This research highlights recommendations for enhancing EGLOT's functionality, such as mitigating time constraints, exploring innovative technologies, and improving user interaction through visual components. Overall, EGLOT has shown promising potential in improving Grade 3 mathematics learning outcomes. This success highlights the potential of artificial intelligence-driven (AI-driven) educational tools to personalize learning and improve educational outcomes globally.