Development of a Hybrid AI System Integrating Chatbot-Based Intelligent Assistants for Adaptive Interaction
TL;DR
This research introduces a hybrid artificial intelligence that incorporates chatbot intelligent assistants based that combines large language model (LLM) with rule-based filtering, allowing it to tailor its conversational responses according to user profiles, interaction histories, and relevant contextual information.
Abstract
The rapid expansion of digital transformation, growing environmental challenges and increasing global demands for sustainability, has created a need for systems that can balance technological performance with environmental responsibility and more intelligence. This research introduces a hybrid artificial intelligence (AI) that incorporates chatbot intelligent assistants based. The proposed system combines large language model (LLM) with rule-based filtering, allowing it to tailor its conversational responses according to user profiles, interaction histories, and relevant contextual information. The system is assessed based on three main criteria: response accuracy, the effectiveness of adaptive personalization, and energy consumption during each inference cycle. The experimental findings that the proposed research increases user satisfaction by 34,7% compared base chatbot and reduces computational consumption by 21,3% through dynamic model routing. The system supports Sustainable Development Goals 1, 4, and 17 by accessible digital services, inclusive education through intelligent tutoring, and collaboration among stakeholders to expand technology adoption in developing economies.