Uli-RL: A Real-World Deep Reinforcement Learning Pedagogical Agent for Children

Deep Reinforcement Learning (DRL) has proven its usefulness in various fields, such as robotic control systems, recommendation algorithms, and natural language dialogue interfaces. Recently, we have been witnessing a growing interest in applying DRL in education, with early results suggesting beneficial effects. However, the majority of research in educational applications apply methods in simulation without evaluation with real learners, thus providing scarce evidence of its effectiveness on real-world problems. Arguably, real-world applications are crucial to properly assess the validity of DRL methods. To this end, we present an approach for integrating DRL into an empirically validated digital reading application for second graders in the form of an adaptive pedagogical agent. We use DRL to tailor the agent’s feedback behavior to each child’s individual learning needs. We evaluate our approach with second graders, investigating their performance and overall motivation, and compare it to a control version of the app. Through this work, we contribute an innovative approach to the use of DRL within the context of primary education, showcasing promising results in a real-world evaluation.