Riiid Research Papers Demonstrate Technology Advancement and Pioneership in the Field of AI for Education

Riiid, a leading AI for education company and a member of Born2Global Centre, announced that six papers by its researchers have been accepted this year at top global AI and EdTech conferences, including International Learning Analytics and Knowledge Conference (LAK), the International Conference on Artificial Intelligence in Education (AIED), and International Conference on Educational Data Mining (EDM). The papers demonstrate the learning effect of AI in education and verification of AI model reliability.

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“AI in education (AIEd) is where diverse fields such as learning science, pedagogy, and cognitive psychology work in a comprehensive manner,” said Jason Juneyoung Park, AI Research Lead at Riiid. “Riiid has been collecting anonymized data since 2015 when AI was not receiving much attention, and we continue to put our focus in AIEd research and widen technological advancement in research capabilities.”

Two papers accepted at LAK 21 in March are studies on predicting learning behaviors and maximizing learning effects. The paper, ‘SAINT+: Integrating Temporal Features for EdNet Correctness Prediction,’ defines and applies data related to learning time such as problem solving time and answer submission time to dramatically increase the accuracy of the answer correctness prediction algorithm. In the other paper titled, ‘Recommendation for Effective Standardized Exam Preparation,’ an algorithm is presented that selects and recommends problems that maximize the learning effect by predicting learners’ learning skills and improve those skills with additional learning tools such as online lecture videos.

At AIED 21, Riiid’s paper was recognized for diagnosing the learner’s learning status with minimum questions and assessing the reliability of the prediction model.

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At EDM 21, a Riiid paper highlights a structure for increasing reliability of AI models and improving learning deviations and score prediction algorithms. In particular, the paper ‘Knowledge Transfer by Discriminative Pre-training for Academic Performance Prediction’ accepted at EDM was nominated for best paper. The paper proposes a method for reducing the error rate compared to the current highest accuracy model by four percent by applying the latest natural language processing technology for the first time in AIEd.

In 2020, Riiid publicly unveiled the world’s largest learning dataset, ‘EdNet’ to pioneer AIEd research efforts and held a global algorithm challenge that incorporates the EdNet. Earlier this year, Riiid hosted a workshop for the development of AIEd at AAAI, one of the world’s most prestigious AI conferences. In August, the formation of EdSAFE AI Alliance to establish a healthy ecosystem in the AIEd industry was announced at the ASU+GSV Summit in partnership with DXtera Institute. Organizations involved in the initiative also include Carnegie Learning, ETS, GSV Ventures, UL, Digital Promise, the German Alliance for Education, and Education Alliance Finland, among others. UNESCO has signed on as mission supporters as well.

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