Developing Emotion-Aware, Advanced Learning Technologies: A Taxonomy of Approaches and Features

Abstract

A growing body of work on intelligent tutoring systems, affective computing, and artificial intelligence in education is exploring creative, technology-driven approaches to enhance learners’ experience of adaptive, positively-valenced emotions while interacting with advanced learning technologies. Despite this, there has been no published work to date that captures this topic’s breadth. We took up this grand challenge by integrating related empirical studies and existing conceptual work and proposing a theoretically-guided taxonomy for the development and improvement of emotion-aware systems. In particular, multiple strategies system developers may use to help learners experience positive emotions are mapped out, including those that require different amounts and types of information about the user, as well as when this information is required. Examples from the literature are provided to illustrate how different emotion-aware system approaches can be combined to take advantage of different types of data, both prior to and during the learner-system interaction. High-level system features that emotion-aware systems can tailor to learners in order to elicit positive emotions are also described and exemplified. Theoretically, the taxonomy is primarily informed by the control-value theory of achievement emotions (Pekrun 2006, 2011) and its assumptions about the relationship between distal and proximal antecedents and the elicitation and regulation of emotion. The taxonomy expands upon a dichotomy of emotion-aware systems proposed by D’Mello and Graesser (2015) and is intended to guide the design of emotion-aware systems that can foster positive emotions during learner-system interactions through the use of varied approaches, data sources, and design features.