Generative Artificial Intelligence in Ubiquitous Learning: Evaluating a Chatbot-based Recommendation Engine for Personalized and Context-aware Education

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.