ProBot integrates a unique multiverse technique and utilizes a speaking-enabled chatbot, leveraging co-learning ontology to foster collaboration between students and robots. Operating on a fuzzy logic-based linear model within a multi-agent reinforcement learning framework (MARL), ProBot delivers personalized communication over high-speed networks, including future-generation networks (6G). Its ontology framework tailors discussions, tests, and exercises to individual learning objectives, enhancing conversational flow and proficiency evaluation through advanced voice creation and natural language comprehension. MARL facilitates adaptive feedback loops among ProBot agents, offering tailored recommendations and learning strategies. ProBot adjusts difficulty levels, identifies improvement areas, and ensures scalable feedback across all proficiency levels. Simulations demonstrate ProBot’s effectiveness in improving student proficiency through varied parameters such as language skills and engagement levels. Experimental results confirm significant proficiency gains, establishing ProBot as a pioneering educational tool that transforms student-robot interactions and supports diverse learning goals in English proficiency.