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Aligning AIED Systems to Embodied Cognition and Learning Theories

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Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky (AIED 2024)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2150))

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Abstract

Embodied Learning involves the development of perceptuo-motor schemas as students manipulate physical objects that embody a concept, or when students move in domain-relevant ways, physically enacting solutions to problems, as well as gesturing as they interact with peers face-to-face. Teaching standards (e.g., for K-12 math education) align with such theories of learning. For instance, teachers are encouraged to have students “talk mathematics”, construct viable arguments and critique the reasoning of others, model with mathematics, and use tools with precision, among others. Yet most learning technologies and tutoring systems are not designed to facilitate embodied learning; instead, they tend to align with more traditional views of student learning. This paper explores possible ways AIED systems can reflect embodied learning theories, within K-12 STEM education. We present the WearableLearning platform as an example that intends to bring embodied learning to mathematics and computing education for students at the K-12 level, with implications and suggestions on how to use Artificial Intelligence to make embodied learning environments more effective.

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Author information

Authors and Affiliations

  1. University of Massachusetts Amherst, Amherst, MA, 01003, USA

    Ivon Arroyo, Injila Rasul, Danielle Crabtree, Allison Poh, Sai Gattupalli & William Lee

  2. New York University, New York, NY, 10012, USA

    Francisco Castro

  3. Assumption University, Worcester, MA, 01609, USA

    Hannah Smith

  4. Worcester Polytechnic Institute, Worcester, MA, 01609, USA

    Matthew Micciolo

Authors
  1. Ivon Arroyo
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  2. Injila Rasul
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  3. Danielle Crabtree
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  4. Francisco Castro
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  5. Allison Poh
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  6. Sai Gattupalli
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  7. William Lee
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  8. Hannah Smith
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  9. Matthew Micciolo
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Correspondence to Ivon Arroyo .

Editor information

Editors and Affiliations

  1. University of Memphis, Memphis, TN, USA

    Andrew M. Olney

  2. University of Duisburg-Essen, Duisburg, Germany

    Irene-Angelica Chounta

  3. Jinan University, Guangzhou, China

    Zitao Liu

  4. UNED, Madrid, Spain

    Olga C. Santos

  5. Universidade Federal de Alagoas, Maceio, Brazil

    Ig Ibert Bittencourt

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Arroyo, I. et al. (2024). Aligning AIED Systems to Embodied Cognition and Learning Theories. In: Olney, A.M., Chounta, IA., Liu, Z., Santos, O.C., Bittencourt, I.I. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky. AIED 2024. Communications in Computer and Information Science, vol 2150. Springer, Cham. https://doi.org/10.1007/978-3-031-64315-6_1

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  • DOI: https://doi.org/10.1007/978-3-031-64315-6_1

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