Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the…

Accounting for complex interactions between contextual variables and learners' individual differences in aptitudes and background requires building the means to connect and access learner data at large scales, across time, and in multiple contexts. This paper describes the ASU Learning@Scale (L@S) project to develop a digital learning network…

A variety of techniques for collecting and analyzing information about the natural use of natural languages is surveyed, emphasizing the importance of recognizing the properties of a research task that make a given technique more or less suitable to it rather than comparing techniques globally and ranking them absolutely. An initial goal is to…