Riiid Labs successfully launched AI education solutions in America

Riiid Labs | July 06, 2020

Riiid, a member company of the Born2Global Centre, could not be coming to America at a better time, as artificial intelligence transforms the global education landscape in 2020. Riiid Labs is the global arm of parent company Riiid and has been established in Silicon Valley to build upon Riiid's success in Asia and to expand its business across the U.S., South America, Middle East and beyond. Riiid is a leading AI edtech start-up based in Korea, which successfully launched Santa for Test of English for International Communication (TOEIC) test prep engine in Korea and Japan and reached No. 1 in sales among education apps in the Android market after only five months. Riiid has been an active member company of the Born2Global Centre since 2016. Riiid's proprietary AI technology analyzes student data and content, predicts scores and user behavior, and recommends personalized study plans in real-time to help students optimize their learning potential. Santa for TOEIC users have improved their score by an average of 124 points after just 20 hours of study. Leveraging the success and proven efficacy of Riiid's AI technology, Riiid Labs will continue to innovate and expand globally across new education verticals and domains.

Spotlight

For nearly a decade, researchers and technologists have been building algorithms designed to help practitioners identify and support those students who are least likely to persist and graduate. As more data has become available, these risk algorithms have become more sophisticated and diverse.In EAB’s work with the 200+ members of the Student Success Collaborative, we’ve found that risk algorithms almost always involve a trade-off between timeliness, breadth of applicability, and feasibility. Algorithms that are built on core student records (such as coursework and grades) apply to a broad number of students and are relatively inexpensive to maintain, but can only be updated a handful of times a year as more transcript data becomes available.

Spotlight

For nearly a decade, researchers and technologists have been building algorithms designed to help practitioners identify and support those students who are least likely to persist and graduate. As more data has become available, these risk algorithms have become more sophisticated and diverse.In EAB’s work with the 200+ members of the Student Success Collaborative, we’ve found that risk algorithms almost always involve a trade-off between timeliness, breadth of applicability, and feasibility. Algorithms that are built on core student records (such as coursework and grades) apply to a broad number of students and are relatively inexpensive to maintain, but can only be updated a handful of times a year as more transcript data becomes available.

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n2y LLC | October 04, 2022

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KU launches online Master of Social Work through Jayhawk Global education innovation center

The University of Kansas | October 11, 2022

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Springboard | October 07, 2022

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