Kids are outsourcing their minds We’re building the layer that makes them think anyway
Before the research catches up.
Before the regulation lands.
Before a generation loses the habit of thinking for itself.
AI literacy isn’t a tools problem. It’s a thinking problem.
Our work is grounded in decision science and design thinking, applied to AI.
Decision science tells us how people actually reason under uncertainty, when they offload cognition, and what makes good thinking durable.
Design thinking gives us the method for co-building interventions with the teachers and students who will use them, testing what works, and iterating until it does.
California has mandated AI literacy in every K–12 classroom. 30+ states are moving on similar bills. The work is the same either way.
for student AI use
metacognition under AI
The evidence is already in. The intervention is what’s missing.
We read it. We operationalize it.
In the classroom. At the policy table.
Co-build the tools and curriculum that teach AI literacy.
We design alongside the teachers and students who will use this work. Not for them, with them. The tools, prompts, and curriculum we build are tested and refined in the classrooms they are meant to serve.
See Our Programs →Draft AI policies that protect student thinking.
Districts are still figuring out what belongs in their AI policies. We help you draft and implement policies that protect students’ cognitive capacities.
See Our Advisory Work →This is an ecosystem problem. We convene the ecosystem. We operationalize the research.
What cognition-first AI actually looks like.
When a student opens any AI tool, the Platform pauses before the answer loads and prompts them to form a position first. Then AI answers. The exchange is scored and logged.
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Teachers build it. Students think with it.
A hands-on teacher fellowship. A student curriculum that builds AI literacy from the inside out. A research pilot that turns every classroom session into evidence.
See Our Programs →A letter from the founder Why I left Google to build this
Protect every student’s right to think for themselves.
A generation that grows up with AI as their most patient tutor: and still arrives at their own conclusions.
AI literacy isn’t a tools problem. It’s a thinking problem.
Our work is grounded in decision science and design thinking, applied to AI.
Decision science tells us how people actually reason under uncertainty, when they offload cognition, and what makes good thinking durable.
Design thinking gives us the method for co-building interventions with the teachers and students who will use them, testing what works, and iterating until it does.
Every framework we build, every audit we run, every classroom we work in starts here.
One learning: the system only moves when every seat works toward the same thing. I know how to convene these players, and I know how to move the system.
Preserving students’ ability to think is too dangerous to get wrong. We need every player at the table: and it’s time we get to work.
I grew up in Brooklyn, between Flatbush and Marine Park, with my mom working in Midwood and my schools spread across all three. A city that can feel like everything is possible and nothing is simple, often in the same hour.
I was lucky to have parents who learned how to navigate the behemoth of NYC public schools. In true Pakistani immigrant tiger parent fashion, they pulled me out of one middle school after two months because they didn’t like how math was being taught, and moved me into a “math lab” at I.S. 240 in Midwood, built on self-directed learning. You were handed a textbook and told to teach yourself. When you got stuck, the rule was unusual: before asking for help, you had to pinpoint the exact step where your logic had slipped. The teachers kept watch and acted as guardrails, but they refused to do our thinking for us.
One moment from 8th grade still sticks. On the Regents, the exam gave us five problems and said to pick four. I did all five, second-guessed a perfectly correct answer, switched it for a different one, and ended up with a 99 on the exam. It was a frustrating lesson in trusting my own gut, and the point: I had been given the space to develop a perspective worth trusting. I came out of that classroom believing my mind was something I could train. My parents hadn’t just secured access to better schools; they had fought for my right to develop a mind of my own.
I spent the next decade in the weeds of NYC classrooms, advising elected officials on school integration and zoning issues, watching how students get sorted early, who gets the advocates, and who gets written off. Then I watched that same dynamic collide with technology.
Inside Google, I worked on AI policy, specifically the socio-emotional dependencies kids were forming with chatbots. I saw a surreal disconnect. Google had spent years pushing devices into American schools; the executives driving that push sent their own kids to low-tech schools that kept screens out of the classroom. As AI arrived, the pattern repeated. Publicly, panacea. Privately, use carefully.
The people shipping these tools to other people’s children were shielding their own.
Well-resourced students are now treating AI the way their parents treat an early draft: something to push against, not something to accept. Not because their schools are teaching it. Because their parents model it. They push back on outputs, ask themselves what they think before accepting what ChatGPT/Claude/Gemini returns, and continue to refine the thinking until they arrive at their own point of view. That scaffolding is domestic, not institutional. For a student whose parents don’t have that fluency, the classroom is the only potential equalizer. But teachers are waiting on guidance. So students ask AI. AI answers. And the answer becomes the thought.
This isn’t a technology problem. It’s an equity problem wearing technology’s clothes.
The mission I’d spent my career on, the battle to ensure every kid has the intellectual agency to use their own mind, is now inside every backpack in America. And the kids with the fewest advocates are the most exposed. The teachers I’ve worked with aren’t the problem; they are the last line of defense. What they lack is a way to turn their pedagogical instincts into data that districts and policymakers can act on.
We are watching this movie in real-time with social media: harm first, research a decade later, regulation a generation too late. I will not watch it twice.
But this trajectory is not inevitable. AI systems can be built to prompt students to form hypotheses, make their reasoning visible, and engage before handing them answers. That friction is not inefficiency. It is the defining product decision of this generation.
So that’s what I built. A layer that sits on top of every AI tool a student already uses, slows them down just enough to think, and gives teachers the visibility to guide them. When a student asks ChatGPT/Claude/Gemini to write their thesis, the layer will ask them first to think through what they think the argument should be. It tracks habits over time and tells teachers which students are learning to think alongside AI and which are outsourcing it. It doesn’t replace the teacher. It gives the teacher something to work with.
I built the first version myself, then found the people who shared the conviction. That’s what we’re building at Augmented Intelligence Advisory: the cognitive guardrails so the next generation can still think for themselves.
We will come to see this as the student equity issue of the 21st century.
Work with us to get it right.
Decision education Built for the age of AI
The THINK Framework is what students learn; the THINK Platform is where they practice it.
The Framework teaches students to recognize when AI is augmenting their thinking and when it is replacing it, organized around five stages drawn from the Alliance for Decision Education’s K–12 Learning Standards.
The Platform operationalizes that model at the point of use: it sits on top of every AI tool a student already uses, pauses before the answer loads, and prompts students to bring something of their own to the interaction.
The pedagogy was already there. THINK is how it becomes measurable.
What We Capture
Seven things, captured every time a student uses AI. The first structured dataset of adolescent metacognition under AI.
Prompt Complexity Score
Every student prompt is scored in real time across four dimensions: Goal Clarity, Context Scaffolding, Reasoning Demand, and Epistemic Posture. The score is visible to the student.
Prompt Complexity Curve
A longitudinal view of how a student’s prompts mature across weeks, units, and subjects. The headline artifact: the first measure of adolescent metacognitive development under AI.
Every score is visible to the student. Every capture becomes a small, stackable lesson in metacognition.
The framework in action, at the point of use.
When a student opens any AI tool, the Platform pauses before the answer loads and prompts them to form a position first. Then AI answers. The exchange is scored and logged.
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Ready to make thinking visible in your district?
We work with district leaders, policymakers, and school systems to stand up the platform, train teachers, and turn the evidence into action.
See How We Work →A Full-Ecosystem Approach to AI Literacy in Schools
Audit the school.
Onboard the teachers.
Equip the students.
AI literacy is not a workshop. It is a system change.
A one-day training does not rewire how a student reasons, how a teacher designs assignments, or how a leader sets policy. The work has to reach all three at the same time, in the same direction, or it does not stick. Schools cannot teach students to think with AI if leaders cannot see how it is already being used, if teachers have not built their own fluency, and if the curriculum is bolted on rather than built in. We work with schools across the full ecosystem: diagnose, onboard, equip, co-design, implement.
Our work is grounded in decision science and design thinking, applied to AI.
Decision science tells us how people actually reason under uncertainty, when they offload cognition, and what makes good thinking durable.
Design thinking gives us the method for co-building interventions with the teachers and students who will use them, testing what works, and iterating until it does.
AI literacy isn’t a tools problem. It’s a thinking problem. We treat it that way.
Step 1: The School-Based AI Audit
Most school leaders are making AI policy decisions without data on what's actually happening in their classrooms. We start by giving them that data.
Surveys & Classroom Mapping
Anonymous student and teacher surveys mapping current AI use, attitudes, and gaps. Classroom observation across grade bands and subject areas.
A Real Picture, Not Anecdote
A confidential report for school leadership: where AI is helping, where it's quietly replacing thinking, and where the policy gaps are. Not opinion. Data.
Where to Start
Together with leadership, we identify which grade bands, subjects, and teachers to start with, and what the rest of the rollout looks like.
Step 2: The THINK Fellowship for Educators
It's not a one-day workshop. It's a partnership where teachers become designers of AI literacy, testing what works in their classrooms and building evidence of real behavior change.
Hands-On Intensive Launch
Teachers build their own AI fluency grounded in cognitive science, then move into a co-design sprint: drafting classroom interventions, testing them with real students, and iterating based on what actually moves behavior change.
Ongoing Community of Practice
Office hours, peer-to-peer problem solving, surveys that measure what's actually changing in classrooms, and a culminating gathering where teachers present classroom data, co-design the next version, and earn their Certified THINK Facilitator credential.
Step 3: The THINK Curriculum for Students
Students design their own AI practice. Test it. Prove it works. Then help other students do the same.
The Core Curriculum
Students see the research that AI is making them think less, then learn the THINK Framework: think, hypothesize, interrogate, navigate, know. They practice with real AI, test their knowledge, remove the scaffolds, and design their own protocol for using AI as a thinking tool.
Ongoing Reinforcement
Surveys and reflection checkpoints help students see how their habits are changing. Our Chrome extension runs alongside their AI use, surfacing the evidence: are they asking better questions, fact-checking more, thinking more? They update their protocol with every checkpoint.
Why an ecosystem, not a workshop?
Real behavior change takes time. Real teaching innovation takes iteration. When the school is audited, teachers are co-designers, and students build their own protocols, schools stop being participants in someone else's program and become designers of their own. By the end, we have proof of what changed.
Interested in partnering?
We work with districts, school networks, and education organizations to bring these programs to classrooms.
Get in Touch →Five ways to work with us One shared thesis
Audits.
Advisory.
Platform.
Programs.
Research.
Five ways in, because no single lever moves a system.
AI literacy isn’t a tools problem. It’s a thinking problem.
Every offering below is grounded in the same lens: decision science and design thinking, applied to AI.
Decision science tells us how people actually reason under uncertainty, when they offload cognition, and what makes good thinking durable.
Design thinking gives us the method for co-building interventions with the teachers and students who will use them, testing what works, and iterating until it does.
The five offerings are different doors into the same work.
The THINK Framework is what students learn: five stages drawn from K–12 decision education that teach students to recognize when AI is augmenting their thinking and when it is replacing it. The THINK Platform is where they practice it: a layer on top of any AI tool that scores every prompt in real time, makes thinking visible, and gives teachers the data to act on what they see.
For districts and schools: contact us about piloting the Platform in your classrooms.
A full-ecosystem AI literacy approach for schools. We diagnose current AI use across your school via a confidential audit, onboard teachers through a hands-on fellowship where they co-design the work, and equip students with a curriculum grounded in the THINK Framework. Each phase generates research data the next one builds on.
For school networks and education organizations: contact us about bringing the full program to your district. See how the programs work →
California passed SB 1288. NYC DOE released preliminary guidance in March. We advise state agencies, legislative offices, and school boards on what implementation actually requires: drafting standards, training teachers, and building the evaluation layer. Evidence-based guidance for the decisions districts are making right now.
For state and local government: SB 1288 implementation support in California and readiness assessments for states preparing AI literacy mandates. Policy framework development, draft standards, expert testimony.
For school boards and superintendents: independent guidance on evaluating AI tools, setting usage policies, and building teacher capacity ahead of state-level mandates.
The first longitudinal dataset measuring how student AI interactions evolve before and after structured literacy intervention. Built from real classroom sessions. Validated against peer-reviewed decision-education research.
For foundations and policy organizations: contact us about research partnership.
We read the research. We turn it into product recommendations your engineers can ship.
We evaluate how your product shapes the thinking habits of its users. Before you ship to classrooms, we assess whether your AI features encourage cognitive engagement or quietly train students to offload. Built for AI companies entering education, ed-tech platforms integrating generative features, and foundations vetting tools before funding deployment.
For AI companies entering education: pre-launch cognitive design review and recommendations for building thinking-first AI features that satisfy emerging state mandates.
For ed-tech platforms: prompt architecture audit, scoring methodology, and redesign recommendations grounded in K–12 decision-education standards.
For foundations and district buyers: independent third-party assessment of tools under evaluation, with a written scorecard you can share with your board.
Start with a 30-minute conversation.
Where your district stands on AI literacy today, what to prioritize next, and how we can help you get there. You walk away with clarity, whether or not you work with us.
The people behind the work
15 years across every layer of education.
Cognitive and technical rigor.
Leadership development, change management, and DEI.
This is an ecosystem problem. It requires ecosystem people.
Ayisha brings 15 years of experience across every layer of the education ecosystem, from students to superintendents to product teams inside big tech.
Sahil brings the technical and design rigor: the layer that turns pedagogy into a working product students and teachers actually use.
Yissely brings leadership development, change management, and DEI: the disciplines that turn a classroom pilot into a district-wide shift. This work requires all three.
Ayisha Irfan is the founder of Augmented Intelligence Advisory. She brings a decade across every layer of the education ecosystem and the conviction that this moment requires every layer to show up.
She spent the last several years inside Google advising product and engineering teams on Search’s generative AI features, watching the tools being built for other people’s children get carefully restricted from the children of the people building them.
Before Google, at Airbnb, she led North America local policy and partnerships. Her work included Airbnb’s public commitment to provide accommodations for 20,000 Afghan newcomers and the national public-private partnerships that housed first responders and survivors of domestic violence and human trafficking during COVID-19. This experience shaped her understanding of how tech can be used as a tool for public service.
Prior to that, she spent a decade in NYC public education: organizing students, piloting programs in classrooms, leading leadership development work with current and former teachers, and advising elected officials on educational equity issues.
She holds a master’s in Policy and Applied Statistics from NYU and a bachelor’s in Biology from Brooklyn College.
Sahil Shah is a software engineer, and the Technologist in Residence at Augmented Intelligence Advisory. His work spans more than a decade of building and scaling systems across industries, including fraud detection, payments, search, and business analytics. He focuses on solving ambiguous problems, working across stakeholders, and delivering solutions that balance short-term iteration with long-term sustainability.
Sahil has held engineering roles at Meta Platforms, Workday, and Airbnb. At Airbnb.org, he was a technical lead on the org’s commitment to provide emergency housing to 100,000+ people displaced by disasters and conflict. This work reinforced his interest in applying technology to real-world needs.
He currently works as a software engineer at The New York Times, focusing on AI, data engineering, and digital archiving.
Sahil holds a degree in Computer Engineering from University of California San Diego.
Yissely Ortiz is Chief Programs Officer at Augmented Intelligence Advisory, where she leads program strategy and designs educator- and student-facing learning experiences at the intersection of AI, critical thinking, and educational equity.
She brings more than 14 years of cross-sector experience in leadership development, inclusive program design, and systems change. Her work pairs deep expertise in facilitation and curriculum design with a gift for translating complex ideas into accessible learning.
With deep relationships across New York City government and education leadership, she is especially energized by building programs that help educators and students navigate change with confidence, clarity, and agency.
Let’s talk about what comes next.
AI companies building for classrooms. Ed-tech platforms designing their next product. State agencies implementing new mandates. Foundations backing what comes after the hype. Districts and educators ready to stop watching and start building.
If that’s you, let’s talk.
Get in Touch →Audits · Advisory · Platform · Programs · Research