Improved Performances and Motivation in Intelligent Tutoring Systems: Combining Machine Learning and Learner Choice

This paper presents ZPDES, an AI-driven intelligent tutoring system that personalizes exercise sequencing based on learning progress and multi-armed bandit algorithms, and evaluates its impact on both learning outcomes and intrinsic motivation in a large-scale randomized controlled trial with 265 children aged 7-8. The study specifically examines how incorporating learner choice affects learning performance and motivation when combined with adaptive personalization versus fixed curricula.

Large class sizes challenge personalized learning in schools, prompting the use of educational technologies such as intelligent tutoring systems. To address this, we present an AI-driven personalization system, called ZPDES, based on the Learning Progress Hypothesis - modeling curiosity-driven learning - and multi-armed bandit techniques. It sequences exercises that maximize learning progress for each student. While previous studies demonstrated its efficacy in enhancing learning compared to han

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