Multimodal Interfaces for Inclusive Learning

In this paper, we propose that the artificial intelligence in education (AIED) community lead the charge in leveraging multimodal interfaces, in conjunction with artificial intelligence, to advance learning interfaces and experiences that are more inclusive. Recent years has seen the development of various multimodal technologies for capturing voice, gesture and gaze-based input modalities, as well as various forms of auditory and haptic feedback. These modalities could be powerful tools for developing inclusive learning interfaces. To ground this idea, we present a set of examples for how this work can be transformative in democratizing access to technology, while also democratizing designing and building technology. Additionally, our examples reinforce how designing for inclusive learning can result in improved learning interfaces for the general population.