Artificial Intelligence in Education(AIED)is scaling to large numbers of persistent users,which offers new opportunities and new difficulties.As AIED scales,it will need to follow recent trends in service-oriented and ubiquitous computing: breaking AIED platforms into distinct services that can be composed for different platforms(web,mobile,etc.)and distributed across multiple systems.This will represent a move from learning platforms to an ecosystem of interacting learning tools.Such tools will enable new opportunities for both user-adaptation and experimentation.Traditional macro-adaptation(problem selection)and step-based adaptation(hints and feedback)will be extended by meta-adaptation(adaptive system selection)and micro-adaptation(event-level optimization).The existence of persistent and widely-used systems will also support new paradigms for experimentation in education,allowing researchers to understand interactions and boundary conditions for learning principles.The ability to perform this type of ongoing and powerful research studies will completely change the landscape of educational research.New central research questions for the field will also need to be answered due to these changes in the AIED landscape.This talk will describe recent efforts to tackle some of these research questions,including three recent integrations of multiple separate intelligent tutoring systems.Successes and challenges will be discussed,from the perspective of technological,social,and economic factors.