AI and Education: Grand Challenges

The educational applications of AI—typical of much AI research—are a combination of what Pasteur’s Quadrant describes as use-inspired basic and pure applied research. Figure 1 illustrates the way educational problems drive AI in education (AIED) research to create new learning systems as well as the foundational knowledge that will enable them to be built. The fi gure also introduces the term intelligent tutoring systems (ITS), which is generally considered a near synonym of AIED. The figure shows how we can create AIED/ITS systems by drawing upon existing AI theories, tools, and techniques (represented by the downward arc at the lower left, showing pure applied research). Equally, the demands of the systems may drive researchers to make advances in fundamental AI, as indicated by the upward arc at the lower right, representing use-inspired basic research.