Effectiveness of an Adaptive Learning Chatbot on Students’ Learning Outcomes Based on Learning Styles

Intelligent learning systems provide relevant materials to students based on their individual pedagogical needs and preferences. However, providing personalized objects learners’ preferences, such as styles which are particularly important for the recommendation of objects, re-mains a challenge. Recommending most appropriate learners has always been challenge in field e-learning. This driven educators researchers implement new ideas help improve experience knowledge. New solutions use artificial intelligence (AI) techniques machine (ML) natural language processing (NLP). In this paper, we propose develop personalization approach that implements adaptation according style mainly focused chatbot, named LearningPartnerBot, will be integrated into Moodle platform. We Felder-Silverman Learning Styles Model determine order recommend also overcome cold start problem. A chatbot is an automated communication tool attempts imitate conversation by detecting intentions its user. The proposed LearningPartnerBot should able answer questions real time set suggestions needs.