AI in the Classroom

We explore a central tension in AI and writing instruction: if educators want authentic student work, is handwriting really the answer?

Using research on handwriting versus typing, plus reflections on Chris Lehman’s argument for transparency in schools, we examine why a “return to the blue book” may create new problems instead of solving the old ones.

Topics covered in this episode:

• Why concerns about AI-generated writing are pushing some schools back toward handwritten assessments
• What research says about handwriting, typing, memory, and composition
• The difference between writing for learning and writing for composing ideas
• Why handwritten work may disadvantage some students
• Equity concerns for students who rely on digital accommodations
• The tradeoffs between preventing AI misuse and preserving authentic writing practice
• Why transparency may be a better long-term strategy than banning tools
• What Science Leadership Academy’s policy approach can teach schools right now

Sources:

https://www.scribd.com/document/956588967/Att-I4-HF-Mtnl65BnxCQFqgKkCDxgIJY6v4LHufLYmnqy8

https://ies.ed.gov/ncee/wwc/practiceguide/17

https://practicaltheory.org/blog/2026/03/07/ai-and-showing-our-work/

https://pubmed.ncbi.nlm.nih.gov/15823243/

https://psycnet.apa.org/record/2012-04390-002

https://psycnet.apa.org/record/2014-35383-001

http://frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1219945/full

https://www.sciencedirect.com/science/article/abs/pii/S0360131515300920

https://www.edweek.org/technology/teachers-turn-to-pen-and-paper-amid-ai-cheating-fears-survey-finds/2023/10

We reflect on Chris Lehman’s recent writing about the state of AI in education and explore a practical middle ground between AI hype and total rejection. We look at why schools are still wrestling with cheating, misaligned expectations, and the real limits of “innovative” lesson design, while also asking where AI may genuinely help, especially in assessment and writing.

Topics covered:

• Chris Lehman’s argument that AI in schools is “not going well”
• The two camps in AI education: resistance vs. revolution
• Why AI may still hold promise for more dynamic assessment
• The connection between AI-powered feedback and AI-enabled cheating
• Why schools cannot “lesson plan” their way out of AI misuse
• Misalignment between what teachers assign and what students think the work is
• Why transparency with students, staff, and families matters more than ever

• How quickly students can test, bend, and get around AI guardrails
• Why hands-on experimentation helps school leaders make better AI decisions

Source:

https://practicaltheory.org/blog/2026/03/07/ai-and-showing-our-work/

We talk about what happens when district AI guidance arrives after schools have already spent years figuring things out on their own. We look at the newly released New York City Department of Education AI guidance and compare it with how individual schools across the city have already been responding to AI in very different ways.

Topics covered:

• NYC DOE’s newly released AI guidance
• How schools responded before district policy existed
• The difference between proactive, reactive, and passive school responses
• Why delayed policy creates inequity across classrooms and campuses
• The challenge of defining acceptable vs. unacceptable AI use
• What students are telling us about school’s role in preparing them for an AI-enabled world
• Why assignment clarity matters more than ever
• The tradeoffs between paper-and-pencil writing and digital writing
• What school leaders can learn from values-based AI policy design
• Why professional development alone may not be enough

Sources:

https://www.schools.nyc.gov/about-us/vision-and-mission/guidance-on-artificial-intelligence

https://www.chalkbeat.org/newyork/2026/03/23/schools-develop-ai-policies-awaiting-city-guidance/

In this episode we unpack New York City Public Schools’ new red, yellow, green AI guidance framework and explore what it signals for teachers, school leaders, and district administrators across the country.

We look at why this policy matters not just because NYC is the largest school district in the U.S., but because it offers one of the clearest examples yet of how a district is trying to separate prohibited AI uses, human-in-the-loop uses, and approved professional uses. We also discuss what schools can learn from the parts of the policy that draw hard boundaries around grading, discipline, placement, graduation decisions, IEPs, and student data privacy.

Topics covered:

• NYC’s new red, yellow, green framework for AI use in schools
• Why AI cannot be used for placement, discipline, promotion, or graduation decisions
• Why IEPs and 504 plans must remain in the hands of qualified professionals
• The importance of protecting student data from AI model training and monetization
• What it means to keep human judgment in the loop
• Why student AI use falls into a yellow-light category
• Where teachers have a green light to use AI
• What instructional coaches can do to help teachers
• What district leaders should review immediately for liability and policy compliance
• Why professional learning will matter if schools want staff to understand the difference between green, yellow, and red-light uses

Source: NYC DOE

Who is classroom tech really built for?

We reflect on a long-running tension in K–12 EdTech: the gap between what districts buy, what teachers are expected to use, and what students actually experience. We explore what happens when software is designed more for dashboards, monitoring, and accountability than for genuine teaching and learning.

Topics covered in this episode:

• Why critiques of EdTech in 2026 sound a lot like critiques from 2010
• The difference between building for the buyer versus building for the classroom user
• How district purchasing incentives can shape product design
• Why student engagement is often missing from EdTech adoption decisions
• What it means for a teacher to have a clear role during technology-supported instruction
• A contrasting example of when classroom software actually deepened student learning
• How product philosophy and go-to-market strategy can either align or conflict
• Why educators should ask whether a tool serves learning, accountability, or both

We examine the hidden costs of “free” AI tools in schools. The real issue is not just convenience or productivity. It’s what happens when teachers upload student writing, IEP notes, and other sensitive information into tools that districts have not approved and may not fully understand.
Topics covered in this episode:
• Why “free” AI tools are often paid for with user data
• How student writing can become unrecoverable once uploaded to AI systems
• The risks of using AI to generate IEPs and student support plans
• Why shadow AI is becoming a serious district-level threat
• The tension between teacher efficiency and slow-moving district policy
• How unapproved AI use can expose districts to lawsuits and liability
• Why teachers and administrators need stronger AI literacy
• What districts can do to “ring-fence” approved AI tools and coach staff more actively

Source:

https://thejournal.com/articles/2026/02/25/shadow-ai-is-quietly-becoming-k12s-biggest-cybersecurity-risk.aspx

In this episode, we examine a troubling MIT study showing that AI chatbots can respond differently based on who they think they’re talking to, sometimes giving less accurate, more patronizing, or even mocking responses to users described as less educated or non-native English speakers.

This episode looks at:

• what AI bias can look like in practice 
• why district leaders need better ways to evaluate these tools
• how researchers and schools might work together on accountability 
• why AI alignment is not a finished problem 
• what it means when AI systems “get to know” users over time

We take a closer look at Humble ISD’s AI pilot with OpenAI and ask the deeper questions that often get missed in early conversations about district AI adoption.Topics covered in this episode: • Humble ISD’s pilot of ChatGPT for teachers • Why “time savings” is not enough as a success metric • What districts should measure beyond efficiency • Whether AI-created time actually leads to better student support • The difference between implementation success and instructional success • Risks of teacher overreliance on AI tools • How AI could affect teacher agency and lesson planning • Why districts need guardrails, transparency, and community discussion • The importance of aligning AI pilots to district mission and values • What to do if AI pilots show only marginal gains in student outcomes

Nearly 6 in 10 teens say AI cheating is happening at their school. But what if the real issue is not just cheating, it’s confusion about what schoolwork is actually for?

We explore a powerful idea from Tony Frontier’s AI with Intention: fidelity. When students use AI to summarize a text, generate ideas, or speed up part of an assignment, are they cheating, or are they responding to unclear expectations about the purpose of the work?

Topics covered

• Pew research on AI cheating in schools
• Fidelity vs. cheating
• Assignment purpose and teacher communication
• AI as a shortcut vs. AI as a support
• Reading comprehension, writing, and hidden learning loss
• Why explicit expectations matter
• Implications for classroom practice and district policy

Pew research

AI with Intention: Principles and Action Steps for Teachers and School Leaders

We explore Google’s new AI-suggested feedback feature in Google Classroom and ask the bigger question: will tools like this actually improve writing instruction, or just make it easier to automate teacher comments?

Topics covered:

• Google’s new AI writing feedback in Google Classroom
• Whether AI can reduce teacher workload in meaningful ways
• Why rubric-based feedback may matter more than chatbot-style interaction
• The risk of teachers “rubber stamping” AI suggestions
• How AI could influence the authenticity of teacher-student communication
  

Source:

https://workspaceupdates.googleblog.com/2026/02/educators-now-get-help-drafting-personalized-guidance-on-written-assignments-with-AI.html