A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

• Published in: arXiv.org
• Main Authors: Iulian Vlad Serban, Gupta, Varun, Kochmar, Ekaterina, Vu, Dung D, Belfer, Robert, Pineau, Joelle, Courville, Aaron, Charlin, Laurent, Bengio, Yoshua
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 06.05.2020
• Subjects: Cloud computing; Computer Science - Artificial Intelligence; Computer Science - Computation and Language; Computer Science - Computers and Society; Computer Science - Human-Computer Interaction; Computer Science - Learning; Domains; Machine learning; Motivation; Natural language processing; Students
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2005.06616

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online. Korbit has been designed to easily scale to thousands of subjects, by automating, standardizing and simplifying the content creation process. Unlike other ITS, a teacher can develop new learning modules for Korbit in a matter of hours. To facilitate learning across a widerange of STEM subjects, Korbit uses a mixed-interface, which includes videos, interactive dialogue-based exercises, question-answering, conceptual diagrams, mathematical exercises and gamification elements. Korbit has been built to scale to millions of students, by utilizing a state-of-the-art cloud-based micro-service architecture. Korbit launched its first course in 2019 on machine learning, and since then over 7,000 students have enrolled. Although Korbit was designed to be open-domain and highly scalable, A/B testing experiments with real-world students demonstrate that both student learning outcomes and student motivation are substantially improved compared to typical online courses.