AI in the Classroom

We explore one of the more promising classroom uses of AI: helping teachers create custom decodable texts for early readers. We look at how AI can make it much faster to generate phonics-aligned reading passages connected to students’ interests and classroom experiences, while also examining why teacher expertise still matters at every step.

Topics:

• How AI can help generate custom decodable texts in minutes
• Why decodable texts matter in early literacy instruction
• The difference between generic published passages and classroom-specific reading materials
• How personalization can increase student engagement and relevance
• Why teacher phonics expertise is essential when using AI
• Common AI mistakes in literacy tasks, including misidentifying sound patterns
• How to prompt AI with clear constraints and instructional goals
• Why iteration and revision are part of effective AI use
• The “80/20 rule” for using AI drafts and teacher refinement
• Why this is a strong low-stakes entry point for educator AI professional learning

Source:

https://www.ascd.org/el/articles/how-to-create-custom-decodable-texts-in-minutes

We explore what a publishing controversy reveals about AI, authorship, and trust — and why educators should pay close attention. Using the case of a novel reportedly found to be largely AI-generated, we examine how the same challenges facing publishers are also showing up in schools.

Topics covered:

• The controversy around an AI-generated novel making it deep into the publishing pipeline

• Why AI detection remains unreliable in both publishing and education

• The difference between detection and attribution

• What “fidelity” means in the age of AI-assisted work

• Why ambiguity around acceptable AI use creates problems for students and professionals alike

• How transparency can reduce misalignment, confusion, and hidden AI use

• Why media literacy now has to be an ongoing practice, not a one-time lesson

• What teachers and school leaders should take away from this moment

Source:

https://www.nytimes.com/2026/03/19/books/ai-fiction-shy-girl.html

Drawing on the work of Lee Shulman and a recent interview with researcher Andrew Lan, we look at why AI can generate content and imitate pedagogy, yet still struggle to understand how real students misunderstand, make mistakes, and learn. We also consider what this means for evaluating EdTech tools, teaching AI literacy, and the future potential of simulated student agents in instructional planning.

Topics covered:

• What pedagogical content knowledge (PCK) is, and why it matters so much in teaching

• Why AI still struggles to simulate authentic student thinking and misconceptions

• How this helps explain the “jagged frontier” of AI in education

• Why many AI-powered education tools may underdeliver in real classroom settings

• The difference between knowing content, knowing pedagogy, and knowing how students learn specific content

• Why current approaches to AI literacy may focus too much on tools and not enough on underlying understanding

• How simulated student agents could eventually help teachers test lessons and prompts before instruction

• Why teacher expertise remains difficult to replicate and especially valuable in the age of AI

Source:

https://the-learning-agency.com/the-cutting-ed/article/5-questions-with-andrew-lan/

We explore what kinds of work should teachers actually offload to AI, and what kinds of work should stay fully human?

We examine the argument that so-called “busy work” is often not busy work at all, but part of the real craft of teaching. We push on that idea by asking where AI can genuinely reduce friction and where it risks weakening judgment, reflection, and professional voice.

Topics:

• Why some educators argue teachers should not offload “busy work” to AI
• Whether writing parent emails, designing assessments, and planning instruction are part of the craft of teaching
• The risk of letting AI replace teacher reflection instead of supporting it
• How AI can reduce friction without replacing the important cognitive work
• The difference between tasks where thinking is the point vs. output is the point
• What schools should consider before encouraging teachers to use AI for efficiency
• How district leaders can distinguish between productive AI use and harmful overreliance

Source:

https://www.edweek.org/technology/opinion-why-teachers-shouldnt-offload-their-busywork-to-ai/2026/03

We explore how “AI slop” is flooding children’s media, and why that matters for classrooms, not just screens at home. We look at a new story from The 74 on the rise of AI-generated kids’ videos that appear educational but are often inaccurate, incoherent, or even unsafe.

Topics covered:

• How AI-generated children’s videos can look educational while teaching nonsense
• Why “educational-looking” is no longer the same as genuinely educational
• What AI slop may do to early learning, comprehension, and concept formation
• Why media literacy may need to start earlier and in simpler ways
• How teachers should evaluate AI-generated instructional materials for coherence, not just accuracy
• Why districts may need clearer policies on AI-generated classroom content
• What families need to understand about trust, quality, and children’s media in an AI world

Source:

https://www.the74million.org/zero2eight/ai-slop-is-flooding-childrens-media-parents-should-be-very-alarmed/

https://www.youtube.com/watch?v=f8Z1EmBmf40

We cover a core tension in AI and writing instruction: just because AI can take feedback and grading work off a teacher’s plate, does that mean it should? We explore what’s gained when teachers stay deeply involved in reading and responding to student writing, and where AI may help without replacing the professional judgment that strong writing instruction depends on.

Topics covered:

• Why writing is one of the hardest and most consequential areas for AI in schools
• What teachers gain by personally reading and diagnosing student writing
• The role of formative assessment in improving writing instruction
• Why feedback timing matters so much for student growth
• The practical limits teachers face when responding to large volumes of student writing
• How AI might help with writing feedback without fully replacing the teacher
• The risks of over-automating grading and feedback
• Bias, inconsistency, and hallucination in AI writing tools
• What teachers and district leaders should weigh before adopting AI for writing

Source:

https://www.edsurge.com/news/2026-02-24-what-students-gain-when-teachers-not-ai-grade-students-work

We explore a deceptively simple but increasingly urgent question: How should schools teach students to use AI?

We dig into a major report arguing that the risks of generative AI in children’s education may outweigh the benefits. From there, we examine the difference between AI-enriched learning and AI-diminished learning, and we ask what happens when schools put the tools ahead of the thinking.

We also look at the risks of a tools-first approach, including cognitive offloading, reduced student agency, and the possibility that schools are teaching students to use AI before helping them understand it. Along the way, we highlight examples from schools taking a more reflective, pedagogically grounded approach.

Topics:

• AI’s risks in education may currently outweigh its benefits
• The difference between AI-enriched learning and AI-diminished learning
• Why a tools-first approach can distort how students and teachers understand AI
• Cognitive offloading and how AI may weaken student thinking and creativity
• Why teaching students to use AI is not the same as teaching them to understand AI
• We discuss the importance of prioritizing agency over agility in AI education
• We ask what all of this means for teachers, school leaders, and district decision-makers

Source:

https://www.washingtonpost.com/opinions/2026/03/10/ai-schools-education-technology-artificial-intelligence/

We explore what it would mean to move beyond using AI to simply speed up traditional tests and instead use it to rethink assessment itself.  We look at how AI could help schools design more authentic, responsive, and instructionally useful assessments.

We also explore a central tension: will AI merely help schools build a faster version of the assessment systems they already have, or can it help educators create something fundamentally better for students and teachers?

Topics covered:

• How AI could reshape assessment beyond efficiency and automation
• Why assessment should function more like coaching than scorekeeping
• The difference between optimizing old systems and innovating new ones
• Why authentic performance tasks matter for student learning
• How AI might help teachers hear more student thinking, more often
• The importance of design intent in AI-enabled assessment
• Why routines, infrastructure, and school culture matter as much as the tool itself
• What “assessment literacy” means for students and teachers
• The promise and limits of AI feedback in supporting deeper learning
• Why the teacher’s role remains essential, even as AI grows more capable

We take a closer look at one of the most repeated claims in AI and education: that AI is saving teachers hours every week. But instead of accepting the headline at face value, I ask a more important question: what should schools actually be optimizing for?

Topics covered in this episode:

• Why claims about AI “saving teachers time” deserve more scrutiny
• The limits of self-reported productivity data
• Why optimizing for efficiency can miss the real point of instruction
• A classroom example of students using AI feedback on writing
• Why AI should function as a thinking partner, not an authority
• The idea of transfer: when AI-supported practice helps students improve independently
• How structured feedback can build student metacognition
• Why students need to evaluate feedback, not just accept it
• What timely, specific feedback makes possible in writing instruction
• Why the best use of AI may be enabling more iteration, reflection, and ownership for students
• What educators risk losing when “time saved” becomes the main metric

Sources:

https://www.washingtonpost.com/nation/2026/02/22/ai-chatbots-teach-writing/

https://danmeyer.substack.com/p/i-dont-believe-this-finding-that

We explore a provocative question at the center of today’s AI conversation in education: when AI boosts productivity, what actually happens to the time it saves? Drawing on a recent workplace study discussed in Harvard Business Review, we examine whether AI reduces workload or quietly expands it, and what that could mean for teachers, school leaders, and the future of work in schools.

Topics covered in this episode:

• Mark Cuban’s framing of two types of AI users
• A workplace study suggesting AI may intensify work rather than reduce it
• Why “time savings” do not necessarily mean less work
• The three forms of work intensification: task expansion, blurred boundaries, and multitasking
• What these patterns could mean for teachers and school staff
• Whether AI efficiency gains actually create meaningful downtime
• How AI tools may begin to co-opt teacher voice and professional judgment
• Why district leaders should think carefully about how work is redesigned around AI

Sources:

https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies-it

https://x.com/mcuban/status/2023750950322889050