Computational Literacies Lab

10: CT and AI

Week 10 (October 26)

This week we take up the final theme of the course, AI. We will explore how AI is conceptualized from within computing education, and how AI education might learn from the successes and failures of CS education.

Weekly update

This video covers where the course stands after this semester's schedule changes and how to book office hours with me, responds to a Week 9 discussion report on NYC's CS for All initiative, and then opens the fourth and final unit of the course, AI, with this week's readings.

Outline

  • 0:16 Logistics: getting back on track. Apologies for posting late in recent weeks and for the schedule shifts that followed, after a rough October of travel and illness at home. The fieldnotes week moved so that nobody still finding a site would be left out, and the content weeks after it moved forward.
    • Facilitators: check in with your group at least three or four days ahead about which readings you'll discuss. It's fine to catch up on readings you didn't get to, or to jump ahead. I'll keep responding to your discussion reports as they come in.
  • 2:15 The rest of the semester.
    • Week 10 (this week): computational thinking and AI.
    • Week 11: AI literacies, following the same move from computational thinking to computational literacies that we made at the start of the course.
    • Week 12: Democracy and computing. If strong public schools are foundational to a democratic society, what do the challenges AI brings to schools mean for democracy, and what should educators do?
    • 3:23 Week 13: your choice. Over the arc of the course, what should we spend more time on? Possibilities include computing in other disciplines, how CS education is implemented on the ground (and the job market for CS teachers), big questions about AI itself and what they mean for guiding young people, or how AI is changing teaching. Send suggestions in your discussion reports, by email, or in an office hours meeting by the end of Week 11, when I'll set the Week 13 readings.
    • Week 14: no class (Thanksgiving). Week 15: we share teaching statements and spend some time with our ethnographic fieldnotes.
  • 5:26 Deadlines.
    • If you have outstanding work, let's get it in, and let me know how I can help.
    • 5:52 Draft teaching statement: sometime in Week 12. Feedback is most useful on drafts, when there's still time to revise, so I'll respond before Thanksgiving. A full draft or a sketchy outline both work. If you haven't settled on an idea yet, let's meet.
    • 7:12 Semi-final teaching statement: Sunday, November 29 (the end of Thanksgiving weekend). This is the version that goes out to the whole class to read and discuss in our final discussion group meetings in Week 15, so this deadline can't be extended. You can keep revising for another week before submitting the final version for a grade.
  • 8:33 Office hours. Book a time on my page (computationalliteracies.net/people/chris). The invitation lands on both our calendars; to cancel or move, decline the invitation or use the link in it. If none of the posted times work, email me and we'll find another. Happy to talk about your teaching statement, anything from the course, or anything else.
  • 9:44 Discussion report: Fancsali et al. (2024). One group organized its discussion around the report's own CAPE framework (capacity, access, participation, experience), a good example of using a framework to organize analysis.
    • 10:01 Which part of CAPE matters most? Each is a prerequisite for the next, so all are necessary; a better question may be where the bottleneck is.
    • 12:11 Computational thinking and math. CS depends heavily on math, and discrete math, seldom taught in high school, might hook students who don't thrive on the algebra-to-calculus path. Integrating computing into English language arts draws instead on media and rhetoric: understanding online political discourse means understanding the algorithms that choose what content you see, network effects, and how engagement-driven feeds have contributed to polarization.
    • 14:24 Barriers to implementation. Staffing, teachers' content knowledge, and school funding structures all have deep research literatures that matter for CS education. Send me specific questions if you'd like to go further.
  • 15:06 Unit 4: AI. This semester's course structure mirrors the field: the unit on CS in schools was compressed to make room for AI. Funding has shifted from CS to AI (national organizations such as CSforALL have closed), following presidential priorities from Obama's CS for All to Trump's emphasis on AI. Many CS educators are asking what's next. The AI conversation is frothy and unsettled right now, perhaps like CS education around 2006.
  • 17:22 This week's question: what is the relationship between computational thinking and AI? Is CT needed for AI education? That depends on whether AI education means understanding what goes on behind the scenes, or just using AI systems, at a moment when some ask whether programming will go away and undergraduate CS enrollment is falling.
  • 19:02 Tedre et al. (2021), "CT 2.0." A conceptual proposal: if CT named the core thought process of computer science, what is the equivalent for AI?
  • 19:37 McCune et al. (2026), "The Fragile Edge of Innovation." A policy-oriented, on-the-ground look at state-level K-12 AI guidance.
    • 20:05 AI education vs. AI in education. Learning about AI is different from how AI changes or is used in education, just as CS education is distinct from using computational media to learn other things. The two are still mixed up, and the work CS for All did for CS education is only beginning for AI.
    • Bring whatever positionality you like: CS educator, teacher of another discipline, or your commitments outside school.
  • 21:42 Next week. Can the lessons of moving from computational thinking to computational literacies help us think about AI literacies?

Readings

McCune, J., Zarch, R., Childs, J., Yadav, A., Marshall, S. L., & Grooms, A. (2026). The Fragile Edge of Innovation: Examining Policy Resilience in State-Level K-12 Artificial Intelligence Guidance. Educational Policy, pp. 1–41.

Tedre, M., Toivonen, T., Kahila, J., Vartiainen, H., Valtonen, T., Jormanainen, I., & Pears, A. (2021). CT 2.0: Upgrading computational thinking for the AI era. Proceedings of the 21st Koli Calling International Conference on Computing Education Research.