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Learning Under Algorithmic Conditions
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Notes

table of contents
  1. Cover
  2. Half Title Page
  3. Title Page
  4. Copyright Page
  5. Contents
  6. Introduction
  7. Part 1. Imitation, Thought, and Reason
    1. 1. Technics and Text: Guided by Gilbert Simondon
    2. 2. Deviation Games: Desire and the Pedagogy of Thought
    3. 3. Number Sense in Large Language Models
  8. Part 2. Bodies, Brains, and Common Sense
    1. 4. Neuro-symbolic Algorithms and the Infant Mind
    2. 5. The Problem of Algorithmic Commonsense Learning
    3. 6. Learning on the Neuromorphic Circuit
  9. Part 3. Curriculum, Control, and Computation
    1. 7. Who Controls the Curriculum for AI? The Limits of Participatory Design for Educational AI
    2. 8. Learning to Program
    3. 9. Computational Thinking and Software Studies
  10. Part 4. Mysticism, Robots, and Genetic Algorithms
    1. 10. Machine Learning Ecologies and Self-Organization
    2. 11. Meaningful Robot Learning
    3. 12. Bioinformatic Algorithms and Educational Genomics
  11. Part 5. Viral Affect and School Interfaces
    1. 13. The Urban Public School as Cybernetic Apparatus
    2. 14. Algorithms and Immediacy
    3. 15. Responsible AI and Learning to Language
  12. Part 6. The Onto-Epistemology of Colonial Instrumental Reason
    1. 16. Machining Coloniality and Learning Otherwise
    2. 17. Noisy Compression and Colonial Violence
    3. 18. Instrumentalizing Colonial Reason
  13. Part 7. Life and the Limits of Computation
    1. 19. Learning in the New Dispersed Prime Time
    2. 20. Machine Learning and the Digital Archiving of Death
    3. 21. Thinking Softly with Incomputability
  14. Part 8. Multimodal Learning with Unruly Tools
    1. 22. Learning by Co-constructing with Stupid (but Useful) Generative AI
    2. 23. Digital Technologies and Perceptual Curation
    3. 24. Technosocial Scotomas in the Algorithmic Age
  15. Part 9. The Disruptive Technical Being of Generative AI
    1. 25. Prompt Battles and the Conundrums of Logos
    2. 26. Machine Learning and Its Operational Diagrams
    3. 27. Algorithmic Creativity, Deception, and Delirium
  16. Acknowledgments
  17. Contributors

Learning Under Algorithmic Conditions

Elizabeth de Freitas, Matthew X. Curinga, Ezekiel J. Dixon-Román, and P. Taylor Webb, Editors

University of Minnesota Press

Minneapolis

London

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The University of Minnesota Press gratefully acknowledges the generous assistance provided for the publication of this book by the University of British Columbia, Columbia University, and Adelphi University.

Chapter 1 contains portions previously published, in modified form, from Elizabeth de Freitas, “Fragile Books and Machine Readers: Trans/in/dividual Reading Tactics in a Complex Technical Milieu,” International Journal of Qualitative Studies in Education 37, no. 6 (2024): 1655–65; reprinted by permission of the publisher (Taylor & Francis Ltd, https://www.tandfonline.com). Portions of chapter 5 were previously published in a different form in Carolyn Pedwell, “The Intuitive and the Counter-intuitive: AI and the Affective Ideologies of Common Sense,” New Formations 112 (2024): 70–93.

Copyright 2026 by the Regents of the University of Minnesota

Learning Under Algorithmic Conditions is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0), https://creativecommons.org/licenses/by-nc-nd/4.0/.
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