Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2005.06616 v1 pith:3YUVBCO2 submitted 2020-05-06 cs.CY cs.AIcs.CLcs.HCcs.LG

classification cs.CYcs.AIcs.CLcs.HCcs.LG
keywords korbitlearningdialogue-basedmixed-interfaceopen-domainstudentsbeendesigned
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PAPPL: Personalized AI-Powered Progressive Learning Platform

    cs.CY 2025-08 reject novelty 4.0 of 10

    PAPPL, an LLM-powered tutoring platform that generates progressive hints from student attempt history, was linked to fewer attempts and higher second-attempt success in a small pavement-engineering pilot, but the evid...

Pith tools