Museums and similar informal learning settings offer opportunities for children and families to learn together in an engaging way. Current exhibits rely mainly on parents, teachers, signage, and staff in science museums to provide support and guidance. Since it is not always feasible to have knowledgeable staff on hand and not all parents have the same knowledge and background, children receive varied support and people often miss the point of the learning experience or activity. This project will develop and research a new genre of Smart Science Exhibits that use artificial intelligence (AI) in an adaptive system to support children in learning science by doing science. The aim of the project is to incorporate AI adaptivity and personalization to maximize inquiry-based STEM learning and engagement in informal learning settings. This research builds on the project team's first Smart Science Exhibit (EarthShake), which uses AI vision to give interactive feedback to visitors based on their actions and guides them through scientific inquiry. In the project's preliminary work, the first smart exhibit demonstrated higher engagement and more learning gains than resulted from a traditional museum exhibit addressing the same scientific content. Smart exhibits can extend and enhance the limited support that staff and parents can provide. This project will develop and investigate adaptive approaches to mixing exploration and AI guidance, which will personalize feedback during constructive exploration. The project will build on learning science techniques and technology, proven in intelligent tutoring systems in formal settings, and apply this to different informal learning contexts. The goal is to provide just-in-time learning support, which will extend the time visitors spend with exhibits, thereby deepening inquiry-based science learning. The project is partnering with science museums and afterschool programs, which will enable thousands of children and families from a wide variety of backgrounds to use the project's smart exhibits each year. Smart Science Exhibits is funded by the Advancing Informal STEM Learning (AISL) program which supports innovative research, approaches, and resources as part of its overall strategy to enhance learning in informal environments.
Many informal learning settings are considering mixed-reality (MR) technologies to increase engagement and understanding of science. Using Smart Science Exhibits, the project will investigate how design choices in mixed-reality systems impact users' engagement and learning of STEM concepts. (Mixed reality is the blending of the physical world and the digital world, enabling interaction between human and artificial intelligence.) Project research will extend current research, which is largely descriptive, by investigating empirical results on learner outcomes. Key research questions are: What types of adaptivity and personalization can improve Smart Science Exhibits and MR systems generally? What balance of exploration and AI guidance is best to maximize enjoyment, engagement and learning? Do findings about the effective features of Smart Science Exhibits generalize to different content areas and informal learning settings? The project will employ user-centered design research, formative evaluation, and controlled experimentation to discover how mixed-reality systems should be designed to best meet visitor and staff needs in informal learning settings including multiple museums and afterschool providers. Data on learner behaviors in mixed-reality experiences in a variety of informal settings will inform the design of Smart Science Exhibits. The project will investigate whether adaptive approaches generalize across content and context to achieve better STEM learning, engagement, collaboration, and productive dialogue. The project will incorporate the team's prior technical research, which developed both vision techniques to track children's physical interactions and interactive pedagogical techniques to provide scaffolds for and reactive feedback on children's inquiry and construction behaviors. New technical research will develop AI techniques for adaptive task selection and personalized feedback that draws on a visitor's history of interaction. Project research and design resources will be widely shared with the science museum educators and designers through presentations at annual conferences and with researchers, developers and others through peer-reviewed journal publications and professional publications.
This Innovations in Development award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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