Disembodied AI and the limits to machine understanding of students' embodied interactions

Abstract

The embodiment turn in the Learning Sciences has fueled growth of multimodal learning analytics to understand embodied interactions and make consequential educational decisions about students more rapidly, more accurately, and more personalized than ever before. Managing demands of complexity and speed is leading to growing reliance by education systems on disembodied artificial intelligence (dAI) programs, which, ironically, are inherently incapable of interpreting students' embodied interactions. This is fueling a potential crisis of complexity. Augmented intelligence systems offer promising avenues for managing this crisis by integrating the strengths of omnipresent dAI to detect complex patterns of student behavior from multimodal datastreams, with the strengths of humans to meaningfully interpret embodied interactions in service of consequential decision making to achieve a balance between complexity, interpretability, and accountability for allocating education resources to children.

Editorial: Ethical design of artificial intelligence-based systems for decision making

“…Nathan and Fourneret and Yvert aim to shed light on critical issues associated with the use of AI systems. Nathan (2023) focus on the limitations of disembodied AI (dAI) in educational systems, which emerged particularly during the COVID-19 pandemic. Such systems have two significant limitations: they struggle to model people's embodied interactions, as they primarily rely on statistical regularities rather than capturing the nuanced nature of human behavior; and they are often black boxes, lacking transparency and predictability when applied to new domains.…”