Developing Eternal Learning Model Trainer System to Support Continuous Knowledge Integration and Cognitive Growth

This study introduces and evaluates the Eternal Learning Model Trainer System (ELMTS), a pioneering approach in data processing and model generation designed to transcend the limitations of human knowledge sharing. ELMTS uses speech-to-text and social media mining together to create detailed digital profiles from real-life and online activities, improving data integration and model accuracy for new ways of continuous knowledge sharing. Moreover, by employing the robust algorithms of GPT 3.5, ELMTS constructs detailed digital profiles, analyzing and interpreting data to recognize individual patterns, preferences, and knowledge areas. This leads to a Question and Answer (QnA) system that acts like a digital twin, reflecting the knowledge and experiences of its human counterpart. The system's performance, evaluated through metrics such as correctness accuracy, relevance, completeness, and linguistic accuracy, displayed significant improvement post-user enhancements, indicating enhanced precision in data handling and improved response quality. User feedback further underscored ELMTS's effectiveness in aspects like adaptability, decision-making, and personal growth while pointing out challenges in information overload and ethical concerns, thus shaping future refinements for the system. ELMTS aims to preserve individual wisdom and contribute to the collective human intellect, offering a novel, sustainable approach to knowledge sharing and learning. This research is crucial to realizing a more efficient, user-centric, and accurate knowledge-sharing platform.