Improving Schools, Inc

10/01/2026

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09/25/2026

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On Sunday, Nikole Hannah-Jones published an essay in ‘The New York Times Magazine’ about her most difficult parenting decision. The Pulitzer Prize–winning journalist and her husband — both of whom are Black and attended racially integrated public schools as children — had chosen in 2016 to send their 4-year-old daughter, Najya, to a high-poverty, mostly Black elementary school in Brooklyn that many of the affluent families in their neighborhood avoided.

Ten years later, Hannah-Jones says the decision has backfired. At the time, the veteran education reporter believed that her presence at P.S. 307 would ensure its continued improvement by shielding the school from neglect. But P.S. 307’s problems were more intractable than even Hannah-Jones anticipated. While slogging through a similar middle-school experience a few years later, Najya angrily confronted Hannah-Jones about the “bad” schools she had been made to attend. “I felt overcome with regret,” Hannah-Jones wrote.

The essay caused an eruption of gleeful Schadenfreude online, writes Zak Cheney-Rice. For many on the right, the beef with Hannah-Jones is not that she thought she could make things better — parental control over schools is an old conservative ideal — but that she would even dare to try. The progressive response has been more complicated. “But the big-picture reality is that Americans have largely given up on racial integration and class equality as societal ideals,” writes Cheney-Rice. “The evidence is everywhere, from the Supreme Court’s evisceration of race-conscious education policies to the lack of meaningful countermeasures at either the community or legislative levels. Hannah-Jones illustrates this point by analogizing her family’s experience to that of Black families who integrated schools during Jim Crow.”

Read more from Cheney-Rice on how Black parents in America found themselves burdened with an unequal system and an uncoordinated push to change it: https://nymag.visitlink.me/-_RP-H

08/31/2026

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MIT has just released a 40-page report on AI in teaching and learning, and its main concern is much larger than cheating.

Students at MIT are already using AI widely. According to the report, some see it as a source of help, efficiency, and creative inspiration. Others feel anxious, pressured, and uncertain about what is allowed.

What caught my attention is how AI appears to be changing the social experience of learning.

The committee heard reports of fewer students attending office hours, participating in discussions, or meeting in study groups. When students turn immediately to a chatbot, they may miss the conversations through which they learn to explain an idea, disagree with someone, accept criticism, and work through confusion.

The report also takes assessment seriously. If AI can produce an essay, solve a problem, write code, or construct a proof, the finished product can no longer tell us enough about what a student understands.

MIT recommends oral exams, portfolios, project demonstrations, conversations about submitted work, drafts, version histories, and regular checkpoints. These approaches give teachers more opportunities to see how the student’s thinking developed.

One concept that appears throughout the report is productive struggle.

Students need to experience some difficulty. They need time to get stuck, try an approach, make a mistake, and revise their thinking. The report warns about “cognitive surrender”: the habit of turning to AI at the first sign of difficulty and allowing it to take over the thinking.

AI can still have an important place in learning. It can provide feedback, help students practise, make learning more accessible, and allow them to undertake projects that were previously too complex. The report describes this as augmentation. The student remains intellectually involved and responsible for the work.

MIT also calls for clear course-level AI policies. Students should know when AI is allowed, limited, required, or prohibitedand why. A single university-wide rule will not work equally well for a poetry seminar, a mathematics course, an architecture studio, and a software-engineering project.

The report advises caution with AI detectors because of false accusations and the distrust they can create. It also asks instructors to disclose when they use AI to create teaching materials, provide feedback, or evaluate student work.

I think this may be the most important part of the report: education is also a social and human practice.

Students learn through relationships, shared work, mentorship, discussion, disagreement, and the slow development of confidence and judgement. A chatbot may provide an answer, but it cannot give students the full experience of becoming members of an intellectual community.

AI-aware education will require much more than adding a paragraph to the syllabus. We need to reconsider what students should learn, how they should learn it, and what evidence will genuinely show that learning has taken place.

Link in the first comment!



Reference

MIT Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training. (2026, August 13). Report of MIT’s Ad Hoc Committee on AI use in teaching, learning, and research training. Massachusetts Institute of Technology.

06/03/2026

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06/01/2026

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05/22/2026

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I'm resurfacing one of the previous guides I shared few weeks ago: Critical Thinking Activities for the Age of AI.

If you haven't seen it, the premise is simple. Plenty of people worry about AI eroding student thinking. This guide flips the problem. It turns the AI interaction itself into the site of critical thinking.

Inside are 13 hands-on activities for faculty and students, each mapped to Robert Ennis's (2015) framework of critical thinking skills: analyzing arguments, judging source credibility, handling ambiguity, evaluating assumptions, recognizing fallacies, and asking clarification questions.

It's free, Creative Commons, and yours to adapt. Grab it via the link in the first comment.

05/11/2026

How to integrate the science of reading, plan for effective review sessions with your students, think about inquiry-based learning, and more.

04/26/2026

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I came across this 2021 paper through a tweet by Mushtaq Bilal and gave it a quick scan.

Horbury and Edmonds tested 26 boys aged 10-11 and found that those who handwrote class notes scored significantly higher on conceptual understanding a week after the lesson than those who typed, with very large effect sizes in both history and biology.

The kicker: both groups produced about the same word count, ruling out volume as the explanation.

The paper isn't about AI, but the cognitive logic maps cleanly onto the AI debate. When students offload mental work to a tool, whether a laptop or a chatbot, the surface output can look fine, but the deeper learning thins out.

Link to the paper in the comments!



References

Horbury, S. R., & Edmonds, C. J. (2021). Taking class notes by hand compared to typing: Effects on children's recall and understanding. *Journal of Research in Childhood Education, 35*(1), 55-67.

04/09/2026

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04/08/2026

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Uncovering the Hidden Curriculum in Generative AI!

The neutrality argument around AI never made much sense to me. Every technology carries embedded design choices that shape what gets communicated and how, and GenAI tools are no exception.

From the technology of the scribe to ChatGPT, no tool has ever been a blank pipe. This is the core of the ethical reasoning I bring into AI discussions with colleagues and students.

Some scholars are reviving an old concept to make sense of what is happening inside large language models: the hidden curriculum.

Warr and Heath have a new paper doing exactly that, and it is the one I am sharing today.

I like the framing and it does carry academic weight. Henry Giroux and Michael Apple built it decades ago, and I did use it in parts of my own doctoral work.

Still, I have always found the word "hidden" a little uncomfortable.

It carries a whisper of intentionality, sometimes even of malice, as if someone behind the curtain is choosing what gets buried.

This feeling is even intense now that the online discourse is fraught with this garbage talk about 'conspiracy theories.'

I always get cautious about language that sounds like a secret agenda lives behind the screen.

GenAI models do reproduce bias. I am not arguing with that. But I would not call it hidden in the conspiratorial sense.

The actual story is simpler and harder to fix.

Western and Euro-centric content makes up the bulk of the internet. This content is full of all social evils you can imagine. AI models train on that data, and the outputs mirror the imbalance back to us.

Guardrails are improving, slowly, and that work is real. The deeper job belongs to us as educators: helping students build the kind of ethical AI literacy that lets them see those imbalances for themselves.

Warr and Heath's paper is especially useful here, because it gives teacher educators a method they can actually run with their students.

Warr and Heath (2025) ran an evocative technology audit on various AI models, asking the models to score student writing across different profiles.

They gave ChatGPT and Gemini the same student writing samples but changed details about the students, like their race, school, or favorite music, to see if the AI would grade them differently. It did.

The models avoided obvious bias when race was named openly, but when the same information showed up through indirect cues, the scores dropped and the feedback turned more bossy and controlling toward students labeled as Black or Hispanic.

So, yes AI tools look fair on the surface but quietly punish students through indirect cues like school type and music taste, which is exactly the kind of bias future teachers need to learn how to spot.

Link in the first comment!

Reference
Warr, M., & Heath, M. K. (2025). Uncovering the hidden curriculum in generative AI: A reflective technology audit for teacher educators. Journal of Teacher Education, 76(3), 245-261.