Organization-Wide Continuous Learning (OWCL): Personalized AI Chatbots for Effective Post-Training Knowledge Retention

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Manoj Kumar Manmathan, Pankaj Agarwal, Suraj Ravi Shiwal, Nitin Bhore, Shagun Singal, Bhaskar Saha


This research paper proposes the Organization-wide Continuous Learning (OWCL) system, employing AI chatbots to enhance post-training follow-up activities for employee knowledge retention. Integrating various functionalities (for continuous learning) and leveraging very large language models (vLLM), OWCL provides personalized learning through spaced repetition of topics, adaptive learning, gamification, etc. Here, we discuss a prototype built on the Gemini API, which demonstrated impressive accuracy (over 94%) in core functionalities like question generation and answer evaluation, showcasing the potential of vLLM to revolutionize post-training activities viz recall, revision, and application. With an overall accuracy of 85%, OWCL presents a balanced and practical approach, harnessing cutting-edge technologies while remaining accessible, resource-efficient, and reasonably fast, ensuring both cost-effectiveness and swift implementation.

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