AI in Education

AI in Education What’s at stake

  1. Education is a phenomenological activity: 

  2. Our compentencies are mediated by how they are perceived

  3. Iris Tabak’s keynote gave lots of examples, ex: our perception of how well we have slept/how hard we have worked out 

  4. Stereotype threat (steele)

  5. The knowledge/expertise we seek to teach 

  6. Historically, it has changed. 

  7. As technology changes (diSessa, material pillar of literacy)

  8. As purposes have changed (boyd, ito studying what youth do with mobile, social networking)

  9. As culture changes (connected learning)

  10. It has points of view, politics, power relationships baked in. 

  11. Culturally-sustaining pedagogy: the case of English

  12. Positioning AI in educational contexts: 

  13. As the source of truth

  14. Putting black-boxed machine learning into practice in educational systems. How is this any different than “I’m an expert, you wouldn’t understand."

  15. As the authoritative phenomenological voice

  16. Dashboards. 

  17. How AI is taken up in existing educational practices

  18. Sorting, labeling. McDermott: The acquisition of a child by a learning disability

  19. Scott (Seeing like a state): Contributes to the insistence on legibility: 

  20. “Learning only happens if it can be measured.” This is not true!

  21. Literally Orwellian: Orwell’s main subject in 1984 is what it’s like to have surveillance inside your head. 

  22. In the ed-tech ecosystem: further entrenching bad practices

  23. Case study: e-textbooks

  24. Case study: assessment

  25. Take ZPD seriously: It’s nonsense 

  26. Epistemology: We have a bad habit of presenting educational content as known and fixed, when it’s actually really dynamic

  27. Machine learning is getting unhealthily fetishized as something

  28. Stanford enrollment numbers

  29. code.org rhetoric

  30. Future possibilities: Situated AI

  31. Participatory systems: Let’s take our cyborg selves seriously. (Harraway)

  32. Cyborg pedagogy: how best to teach in sociotechnical systems

  33. Pea’s argument against scaffolding: Technology doesn’t just make the same practices easier; it transforms our practices. True for teachers and students. 

  34. Embodied, agentic computing

  35. OpenAI study on embodied language learning

  36. Interdisciplinary research:

  37. Case study: seeking common ground between linguistic anthropologists and computational linguists

  38. ex: Khan Academy embedded engineers in schools

  39. Case study: 

  40. Humility: If all this strikes you as ridiculous, you have just diagnosed yourself with a problem. 

  41. What this means for us now

  42. If we want a different future, we have no business making claims about education without speaking to  interpretation and positionality. This means our papers must attend to why we feel justified in making the jump from metrics to meanings. Otherwise get the hell out. 

  43. Situ

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