Imagine that you are an undergraduate who excels at science and mathematics. You could go to medical school and become a doctor, or you could become a teacher. If you are in the U.S., most students would not see these as comparable choices. The average salary for a general practitioner doctor in 2010 was $161,000, and the average salary for a teacher was $45,226. Why would you choose to make a third as much in salary?
While there has been an explosion of impressive, data-driven AI applications in recent years, machines still largely lack a deeper understanding of the world to answer questions that go beyond information explicitly stated in text, and to explain and discuss those answers. To reach this next generation of AI applications, it is imperative to make faster progress in areas of knowledge, modeling, reasoning, and language. Standardized tests have often been proposed as a driver for such progress, with good reason: Many of the questions require sophisticated understanding of both language and the world, pushing the boundaries of AI, while other questions are easier, supporting incremental progress. In Project Aristo at the Allen Institute for AI, we are working on a specific version of this challenge, namely having the computer pass Elementary School Science and Math exams. Even at this level there is a rich variety of problems and question types, the most difficult requiring significant progress in AI. Here we propose this task as a challenge problem for the community, and are providing supporting datasets. Solutions to many of these problems would have a major impact on the field so we encourage you: Take the Aristo Challenge!
Computational Thinking (CT) is considered a core competency in problem formulation and problem solving. We have developed the Computational Thinking using Simulation and Modeling (CTSiM) learning environment to help middle school students learn science and CT concepts simultaneously. In this paper, we present an approach that leverages multiple linked representations to help students learn by constructing and analyzing computational models of science topics. Results from a recent study show that students successfully use the linked representations to become better modelers and learners.
Integrating robotics activities in science curriculum provides rich opportunities to engage students in real world science and help them to develop conceptual understanding of physics principles through the process of investigation, data analysis, engineering design, and construction. In addition, students become more confident learners and develop better problem-solving and teamwork skills. In this paper we describe a successful use of LEGO® MINDSTORMS® in designing robotics-based activities for teaching high school physics classes. Students design and perform novel science investigations with a toolset that helps them achieve a high reproducibility in their experimental designs. Several example projects that utilize LEGO MINDSTORMS are presented.
This paper describes work from the Bridges to Computing project at Brooklyn College of the City University of New York. This project focuses on the transition from high school to college with the intention of encouraging more students to study some aspect of computer science. The Bridges project has both introduced new undergraduate courses into our computer science curriculum and revised existing courses, as well as developed activities for high school students to help better prepare them for college-level computer science. Here, we report on the use of ideas from artificial intelligence implemented within several of these interventions.