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Learning Under Algorithmic Conditions: Contributors

Learning Under Algorithmic Conditions
Contributors
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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

Contributors

  • Craig Carson is associate dean for academic affairs in the Adelphi University Honors College and author of The Aesthetics of Democracy: Eighteenth-Century Literature and Political Economy.
  • Felicity Colman is professor of media arts at University of the Arts London. She is the author of books including Deleuze and Cinema: The Film Concepts and is coeditor of Methods and Genealogies of New Materialisms.
  • Matthew X. Curinga is software developer, associate professor of educational technology and computer science education at Adelphi University, and cofounder of the MIXI Institute for STEM and the Imagination.
  • Elizabeth de Freitas is writer and professor at Adelphi University. Among her books are Posthuman Social Science and Computational Culture: Essays on Methodology, Theory, and Practice and, as coauthor, Mathematics and the Body: Material Entanglements in the Classroom.
  • Edward Dieterle is chief learning scientist at Kovexa.
  • Ezekiel J. Dixon-Román is professor of critical race, media, and educational studies at Teachers College, Columbia University, where he is director of the Edmund W. Gordon Institute for Advanced Study. He is author of Inheriting Possibility: Social Reproduction and Quantification in Education (Minnesota, 2017).
  • Shayan Doroudi is assistant professor in the School of Education at the University of California, Irvine, with a courtesy appointment in the Department of Informatics.
  • David Gauthier is assistant professor of computational media and arts in the Department of Media and Culture Studies, Utrecht University, and a practicing media artist.
  • Cathrine Hasse is professor of anthropology of learning and technology at Aarhus University and honorary professor in techno-anthropology at Aalborg University. She is the author of An Anthropology of Learning: On Nested Frictions in Cultural Ecologies and Posthumanist Learning: What Robots and Cyborgs Teach Us About Being Ultra-social.
  • Talha Can İşsevenler is postdoctoral fellow at the Public Science Project, adjunct assistant professor of sociology at CUNY, and in training to become a psychoanalyst.
  • Goda Klumbytė is an interdisciplinary scholar working between informatics and humanities and social sciences. She is coeditor of More Posthuman Glossary and an editor of the critical computing blog https://enginesofdifference.org.
  • Robb Lindgren is professor of educational psychology and curriculum and instruction at the University of Illinois Urbana-Champaign.
  • Michael Madaio is research scientist at Google Research, working in the Responsible AI and Human-Centered Technology research group.
  • Henry Neim Osman is a doctoral candidate in the Department of Modern Culture and Media at Brown University. He works across history of computing, science and technology studies, and philosophy of technology.
  • Luciana Parisi is professor of literature at Duke University and the author of Contagious Architecture: Computation, Aesthetics, and Space and Abstract Sex: Philosophy, Bio-technology and the Mutations of Desire and coauthor of Algorithmic Space and Its Social Implications.
  • Carolyn Pedwell is professor in digital media in the sociology department at Lancaster University and the author of three books including, most recently, Revolutionary Routines: The Habits of Social Transformation. She is coeditor of The Affect Theory Reader 2: Worldings, Tensions, Futures.
  • Arkady Plotnitsky is distinguished professor of English and of Philosophy and Literature at Purdue University. His latest books are Logos and Alogon: Thinkable and the Unthinkable in Mathematics, from the Pythagoreans to the Moderns and the coedited volume The Quantum-Like Revolution.
  • Julian Quiros is provost postdoctoral fellow at the University of Pennsylvania’s Annenberg School for Communication.
  • Sina Rismanchian is a PhD student at the University of California, Irvine School of Education, with a background in computer science.
  • Warren Sack is media theorist, software designer, artist, and professor of film and digital media at the University of California, Santa Cruz, and the author of The Software Arts.
  • R. Joshua Scannell is assistant professor of media studies at the New School’s School of Media Studies.
  • Gregory J. Seigworth is professor of communication studies in the Department of Communication and Theatre at Millersville University and coeditor of The Affect Theory Reader 2: Worldings, Tensions, Futures.
  • Rebecca Uliasz is postdoctoral research fellow in digital studies at the Digital Studies Institute, University of Michigan.
  • David Wagner is professor in mathematics education and associate dean of education at the University of New Brunswick. He serves as coeditor in chief of Educational Studies in Mathematics.
  • P. Taylor Webb is professor in the Department of Educational Studies at the University of British Columbia. His most recent coauthored book is Algorithms of Education: How Datafication and Artificial Intelligence Shape Policy (Minnesota, 2022).
  • Ben Williamson is senior lecturer in the Centre for Research in Digital Education at the University of Edinburgh, UK. He is author of Big Data in Education: The Digital Future of Learning, Policy, and Practice, coeditor of the World Yearbook of Education 2024: Digitalisation of Education in the Era of Algorithms, Automation, and Artificial Intelligence, and an editor of the journal Learning, Media and Technology.

Annotate

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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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