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

Author:

Nathan Mitchell J.

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.

Publisher

Frontiers Media SA

Subject

Artificial Intelligence

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Embodiment and agency in a digital world;Frontiers in Psychology;2024-09-06

2. Active inference goes to school: the importance of active learning in the age of large language models;Philosophical Transactions of the Royal Society B: Biological Sciences;2024-08-19

3. Extremely Relational Robots: Implications for Law and Ethics;Philosophy & Technology;2024-04-13

4. Counterfactual Thinking for Machines;Causality for Artificial Intelligence;2024

5. Editorial: Ethical design of artificial intelligence-based systems for decision making;Frontiers in Artificial Intelligence;2023-07-24

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