Building Inclusive Learning Systems Using Hybrid Multi-Agent Reinforcement Learning with NLP

Abstract

Digitalization of learning processes in education has created more and more needs for intelligent and inclusive learning frameworks that can meet a wide variety of needs of different cognitive skills, languages, and accessibility of learners. Traditional e-learning frameworks do not provide personalized, adaptable and responsive learning experiences as it use inflexible instruction design strategies. To solve the mentioned problems, this study propose an intelligent inclusive learning framework based on hybrid multi-agent reinforcement learning combined with natural language processing. Proposed intelligent learning framework uses multi-agent reinforcement learning along with natural language processing techniques like sentiment analysis, multilingualism, and conversational educational assistance. Specialized intelligent agents such as a learner profiling agent, content adaptation agent, engagement monitoring agent, accessibility agent, and collaborative learning agent are utilized in the methodology, where all these intelligent agents operate in a reinforcement learning setting. The use of NLP modules helps improve the system by facilitating context analysis for interactions, emotional state detection, machine translations, and adaptive feedback, hence enhancing communication quality. Evaluation was conducted through interaction data from students, academic performance data, discussion processes, and accessibility-based learning scenarios. Performance of the proposed framework was compared with e-learning systems, single agent reinforcement learning, and deep learning adaptive tutor systems. Intelligent agents including learner profiling agent, content adaptation agent, engagement monitoring agent, accessibility agent, and collaborative learning agent were used in the methodology, and in all these agents work under the reinforcement learning approach. Application of NLP modules is vital for improving the system since it will help in carrying out context analysis during interactions, emotional state recognition, machine translations, and adaptive feedback for better communication quality. Assessment involved the collection of data from interactions among the students, academic performance data, discussions, and accessibility-based learning situations. The performance of the proposed method was benchmarked against e-learning platforms, single agent reinforcement learning, and deep learning adaptive tutor system.