Beytekin, MusaOztemiz, Furkan2026-04-042026-04-042025979-833158990-5https://doi.org/10.1109/IDAP68205.2025.11222226https://hdl.handle.net/11616/1080699th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321Artificial Intelligence(AI)-based technologies offerthe potential to transform language learning processes. How ever, there is a notablegap in the current literature regardinglanguage learning systems where users simultaneously interactwith multipleAI characters. This research conducts a comprehensive literature review analyzing existing conversational AIs,LangChain/LangGraph frameworks, and multi-character interaction systems. To address these gaps, a hybrid language learning platform development approach is proposed where userscan engage in context-sensitiveand consistent dialogues with multiple artificial intelligence characters simultaneously. In the experimental part, emotion analysis performances of five different large language models were comparatively evaluated foroptimal model selection. Test results revealed that O1 seriesmodels showed superior performance especially in negative-emotions (95-98%), while all models struggled with complexemotional state analysis (54-61%). The main contribution isaddressing the multi-character interaction gap in literatureand providing an empirical foundation for data-driven modelselection in hybrid systems. © 2025 IEEE.trinfo:eu-repo/semantics/closedAccessConversational AILangChainLangGraphLanguage learningMulti-character interactionVirtual charactersConference Object10.1109/IDAP68205.2025.112222262-s2.0-105025035512N/A