Artificial Intelligence in Education

Artificial Intelligence (AI) technologies have been researched in educational contexts for more than 30 years (Woolf 1988; Cumming and McDougall 2000; du Boulay 2016). More recently, commercial AI products have also entered the classroom. However, while many assume that Artificial Intelligence in Education (AIED) means students taught by robot teachers, the reality is more prosaic yet still has the potential to be transformative (Holmes et al. 2019). This chapter introduces AIED, an approach that has so far received little mainstream attention, both as a set of technologies and as a field of inquiry. It discusses AIED’s AI foundations, its use of models, its possible future, and the human context. It begins with some brief examples of AIED technologies

A framework for conceptualizing, representing, and analyzing distributed interaction.

The relationship between interaction and learning is a central concern of the learning sciences, and analysis of interaction has emerged as a major theme within the current literature on computersupported collaborative learning. The nature of technology-mediated interaction poses analytic challenges. Interaction may be distributed across actors, space, and time, and vary from synchronous, quasi-synchronous, and asynchronous, even within one data set. Often multiple media are involved and the data comes in a variety of formats. As a consequence, there are multiple analytic artifacts to inspect and the interaction may not be apparent upon inspection, being distributed across these artifacts. To address these problems as they were encountered in several studies in our own laboratory, we developed a framework for conceptualizing and representing distributed interaction. The framework assumes an analytic concern with uncovering or characterizing the organization of interaction in sequential records of events. The framework includes a media independent characterization of the most fundamental unit of interaction, which we call uptake. Uptake is present when a participant takes aspects of prior events as having relevance for ongoing activity. Uptake can be refined into interactional relationships of argumentation, information sharing, transactivity, and so forth. for specific analytic objectives. Faced with the myriad of ways in which uptake can manifest in practice, we represent data using graphs of relationships between events that capture the potential ways in which one act can be contingent upon another. These contingency graphs serve as abstract transcripts that document in one representation interaction that is distributed across multiple media. This paper summarizes the requirements that motivate the framework, and discusses the theoretical foundations on which it is based. It then presents the framework and its application in detail, with examples from our work to illustrate how we have used it to support both ideographic and nomothetic research, using qualitative and quantitative methods. The paper concludes with a discussion of the framework’s potential role in supporting dialogue between various analytic concerns and methods represented in CSCL