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Abstract Adaptivity in advanced learning technologies offer the possibility to adapt to different student backgrounds, which is difficult to do in a traditional classroom setting. However, there are mixed results on the effectiveness of adaptivity based on different implementations and contexts. In this paper, we introduce AI adaptivity in the context of a new genre of Intelligent Science Stations that bring intelligent tutoring into the physical world. Intelligent Science Stations are mixed-reality systems that bridge the physical and virtual worlds to improve children’s inquiry-based STEM learning. Automated reactive guidance is made possible by a specialized AI computer vision algorithm, providing personalized interactive feedback to children as they experiment and make discoveries in their physical environment. We report on a randomized controlled experiment where we compare learning outcomes of children interacting with the Intelligent Science Station that has task-loop adaptivity incorporated, compared to another version that provides tasks randomly without adaptivity. Our results show that adaptivity using Bayesian Knowledge Tracing in the context of a mixed-reality system leads to better learning of scientific principles, without sacrificing enjoyment. These results demonstrate benefits of adaptivity in a mixed-reality setting to improve children’s science learning. 

Museum exhibits encourage exploration with physical materials typically with minimal signage or guidance. Ideally children get interactive support as they explore, but it is not always feasible to have knowledgeable staff regularly present. Technology-based interactive support can provide guidance to help learners achieve scientific understanding for how and why things work and engineering skills for designing and constructing useful artifacts and for solving important problems. We have developed an innovative AI-based technology, Intelligent Science Exhibits that provide interactive guidance to visitors of an inquiry-based science exhibit. We used this technology to investigate alternative views of appropriate levels of guidance in exhibits. We contrasted visitor engagement and learning from interaction with an Intelligent Science Exhibit to a matched conventional exhibit. We found evidence that the Intelligent Science Exhibit produces substantially better learning for both scientific and engineering outcomes, equivalent levels of self-reported enjoyment, and higher levels of engagement as measured by the length of time voluntarily spent at the exhibit. These findings show potential for transforming hands-on museum exhibits with intelligent science exhibits and more generally indicate how providing children with feedback on their predictions and scientific explanations enhances their learning and engagement.