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A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

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arxiv 2203.03724 v1 pith:ZSRU3326 submitted 2022-03-03 cs.CY cs.AIcs.HCcs.LG

classification cs.CYcs.AIcs.HCcs.LG
keywords learningplatformpersonalizedactiveexperiencekorbitmillionsonline
verification ladder T0 review T1 audit T2 compute T3 formal
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Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning. This is especially relevant in the context of online learning platforms. In this paper, we present the results of a comparative head-to-head study on learning outcomes for two popular online learning platforms (n=199 participants): A MOOC platform following a traditional model delivering content using lecture videos and multiple-choice quizzes, and the Korbit learning platform providing a highly personalized, active and practical learning experience. We observe a huge and statistically significant increase in the learning outcomes, with students on the Korbit platform providing full feedback resulting in higher course completion rates and achieving learning gains 2 to 2.5 times higher than both students on the MOOC platform and students in a control group who don't receive personalized feedback on the Korbit platform. The results demonstrate the tremendous impact that can be achieved with a personalized, active learning AI-powered system. Making this technology and learning experience available to millions of learners around the world will represent a significant leap forward towards the democratization of education.

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  1. Adaptive Experiments Under Data Sparse Settings: Applications for Educational Platforms

    cs.LG 2025-01 reject novelty 4.0 of 10

    WAPTS reweights Thompson Sampling draws by empirical success rate to favor high-performing treatments, claiming faster convergence to near-optimal alternatives in sparse educational experiments.

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