THE FUTURE OF EDUCATION: PERSONALIZED LEARNING THROUGH ADAPTIVE INTELLIGENT TUTORING SYSTEMS WITH NATURAL LANGUAGE AND DEEP LEARNING

Umberto Barbieri, Emanuele Marsico, Luigi Piceci, Raffaele Di Fuccio, Francesco Peluso Cassese

Abstract


The present study fits into the context of Adaptive Learning (AL) and Intelligent Tutor Systems (ITS), focusing on the effectiveness of an Artificial Intelligence (AI) conversational agent capable of emulating a virtual tutor, in optimizing students' learning processes. In a controlled context, we performed a pilot study on a sample of 28 students that was divided into an experimental group, which interacted with the AI system called Albert, and a control group, which followed a conventional learning approach with a human tutor support in presence. The chatbot was developed using Deep Learning models with multi-layered neural networks, allowing for personalized interaction with students. Statistical analysis shows that the experimental group achieved higher scores in all tests to evaluate the knowledge learned, with significantly reduced task completion time. These results indicate the promising role of the virtual tutor as an innovative tool to foster student learning, underlining the importance of incorporating advanced technologies into educational contexts.


Keywords


Adaptive Learning, Personalized Learning, Intelligent Tutor Systems, Deep Learning, Natural Language

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DOI: https://doi.org/10.32043/gsd.v8i3.1179

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