Claims

• Rushed school AI plans can worsen wellbeing and equity risks

• Adult AI productivity gains do not automatically justify the same use for students

• AI adoption in schools is mostly a people-change problem

• AI can reshape a student’s purpose, but it should not replace it

• Take-home essays are no longer reliable evidence by themselves

• Schools should start with learning values before choosing AI tools

• AI changes how people come to know things, not just how fast they work

• AI conversations can become assessments when students have to think visibly

• AI literacy only works when it is connected to subject-area knowledge

• Students should check AI claims against trustworthy sources

• Schools should separate AI literacy work from assessment integrity work

• AI literacy takes system capacity, not just tool access

• AI literacy should teach students what to do with AI, not just what to think about it

• Student AI misuse may signal pressure or unclear purpose

• AI tools should be tested on the real tasks they will be used for

• AI speed can make people feel guilty for thinking slowly

• AI-assisted homework requires redesign, not just policing

• AI-built models can help students show conceptual understanding

• In an AI world, assessment should focus on watching students think

• AI-era media literacy needs emotional resilience too

• Students need boundaries for when to use AI and when to step back

• Treating AI like a person can help if students know the limits

• Children need protected boredom and unscripted play

• Subject-specific AI literacy frameworks are useful maps, not final answers

• New AI capabilities will often reach schools through existing tools

• Educators need sustainable ways to keep up with AI

• Prompting AI is a literacy practice, not just a technical trick

• District AI implementation needs living guidance and teacher-led redesign

• Unequal access to frontier AI can widen educational inequity

• Co-creative AI needs shared concepts about the world

• Human-authored literature gives students a real other person to encounter

• Learning requires some productive struggle that AI can remove

• Failed AI pilots are useful evidence

• Treating normal AI use as pathology can lead to worse school policy

• Prompts can help people coordinate their thinking

• Voice AI can make learning support easier to reach

• Research prompts can support inquiry without taking over student judgment

• Schools need a mix of structured and open-ended AI experiences

• Seamless AI spaces can make AI feel like the authority

• AI can undermine learning when students use it without guidance

• “Use case” language can hide what AI adoption changes

• Vibe coding is an early sign of broader knowledge-work change

• AI chat transcripts can be taught like texts

• AI-generated text can make finished writing less trustworthy as evidence

• AI grading systems need transparency, validation, and bias checks

• AI can make school feel more transactional

• Teen AI use is already normal enough for schools to plan around it

• AI can make a poorly framed problem worse

• Prompt-and-rubric writing is especially vulnerable to AI shortcuts

• AI cannot be trusted to verify exact quotes by itself

• Agentic AI can preserve thinking when students have to design the work

• Technoskepticism gives schools a middle path on AI

• Unreliable AI feedback can damage trust in learning

• Punitive AI bans can drive student use underground

• The interface can limit how useful an AI tool really is

• Follow-up prompts should push AI inquiry toward evidence