Generative Artificial Intelligence in Ubiquitous Learning: Evaluating a Chatbot-based Recommendation Engine for Personalized and Context-aware Education
Manel Guettala, Samir Bourekkache, Okba Kazar, Saad Harous · Acta Informatica Pragensia · 2025
Background: Ubiquitous learning environments aim to provide personalized and context-aware educational resources; however, traditional recommendation systems often fall short in meeting these dynamic learner needs.Objective: This study develops and evaluates a chatbot-based recommendation system that uses generative AI and prompt engineering techniques to enhance recommendation accuracy and user engagement in ubiquitous learning contexts.Methods: A ChatGPT-powered chatbot was implemented using few-shot prompting and dynamic context integration to deliver personalized, real-time educational support.The system was deployed using an intuitive Gradio interface, facilitating user accessibility and seamless interaction across varied learning scenarios.A tailored evaluation dataset was constructed to capture diverse user interactions and the system was tested through real-world case studies and user feedback metrics, including task success rates, response times and satisfaction ratings.Results: The chatbot achieved an 85% overall task success rate, a 70% success rate in context-aware tasks and an 80% user satisfaction rating, with most users assigning scores of 4 or 5 on a 5-point scale. Conclusion:The findings demonstrate that the proposed solution outperforms traditional systems in delivering personalized, adaptive and context-aware educational recommendations, underscoring the transformative potential of generative AI in advancing learner-centred ubiquitous learning environments.