Hi, I’m Angela Chen! I’m an edtech founder, Stanford instructor, and AI in education advisor. Every week, I read across the full AI-in-education landscape: research, classroom practice, startup and funding signals, policy, and market intelligence.

I’ve been doing this privately for my own work and a few people kept asking what I was tracking. So I’m sharing the signals I actually think matter, synthesized across everything I read that week!

If there’s something here worth going deeper on, comment below or DM me on LinkedIn and tell me which signal — I’m always interested in what’s actually landing for people in the field.

The mood this week is pressured: the first wave of AI contracts is hitting renewal and nobody has the evaluation language to justify — or push back on — what they’re spending. At the same time, the intellectual framing is shifting. Horn in front of Congress, Mollick rewriting his thesis, PE betting on human connection as AI saturates digital channels — the field’s most credible voices are all arriving at the same place: adoption without redesign isn’t a strategy, it’s a delay.

⚡ What to pay attention to right now

The university AI contract renewal crisis is here and nobody has the evaluation language for it

California State University just renewed its OpenAI contract for $39M over three years — the largest university-AI deal on record — despite a 4,000-signature faculty petition, active budget cuts, and zero published evidence that the first contract delivered on its workforce readiness thesis. CSU’s troubles are indicative of the sector-wide problem. The first generation of enterprise AI contracts is hitting renewal cycles at the exact moment that evidence standards for what these deals should produce remain undefined. EdSource flagged the gap directly: 94% of higher ed workers now use AI tools, but fewer than half know their institution’s governing policies. If you’re advising an institution right now, push for an evaluation framework before the next renewal conversation begins.

Michael Horn testified before Congress and made the case I’ve been making to advisory clients for years

Horn is the co-founder of the Clayton Christensen Institute and brought disruptive innovation theory to education — his framework shaped how a generation of education reformers and investors think about edtech. His written testimony before the House Subcommittee on Higher Education this week highlighted colleges that adopted AI without redesigning their operating models are seeing the same non-results as steam-powered factories that bolted electric motors onto old machinery. Productivity didn’t come until the factories redesigned around electricity.

He named specific pivots — live oral defenses replacing AI-written essays, AI as assessment infrastructure rather than detection adversary, embedded real-world work — and called out startups like Protagonist (AI-powered student success infrastructure) as early movers worth watching. Horn’s is the clearest public articulation I’ve seen of why the “wait and see” campus posture invites strategic failure, not prudent caution. When this argument reaches Congress, it’s going to start shaping what funding conditions look like and the direction of policy.

Ethan Mollick’s new framing is the one educators need to sit with

Mollick is a Wharton professor whose Substack One Useful Thing is probably the most widely-read practitioner resource on AI in education — his first book Co-Intelligence genuinely shaped how most educators onboarded AI in 2024-2025. His new book, Co-Existence (out October 20), marks a real shift. Co-Intelligence was premised on human-AI collaboration — prompting, editing, skepticism, with humans at the center. Co-Existence is about well…co-existing with AI that is sometimes better than you. He points to Anthropic’s own data that AI now writes 80% of their code, with each developer shipping 8x more.

The education implication is direct: if the “co-intelligence” framing shaped how most educators onboarded AI in 2024-2025, the co-existence framing needs to shape how we redesign curriculum for 2026-2027. The question isn’t “how do students use AI as a tool” anymore. It’s “what does learning look like when AI is sometimes the better practitioner.”

This connects to something Grow with Google surfaced in a recent EdTech Insiders episode: only 5% of workers currently qualify as “AI fluent” — meaning they can actually create and solve problems with AI, not just use it passively or demonstrate surface-level AI literacy. Literacy gets you reading the language. Fluency is when you start thinking in it. That’s the gap every educator is now being asked to close, and most curriculum wasn’t designed with it in mind.

The PE money betting on human connection is the contrarian read this week

Hellman & Friedman acquired Hyve Group — which owns both Bett and ASU+GSV — from Providence Equity and Searchlight Capital. H&F’s thesis is that face-to-face professional events will become more valuable as AI saturates digital channels. On the day the deal closed, they announced Bett USA, a new annual K-12 event for US educators.

The signal isn’t just market consolidation. It’s that PE is making the same bet as the smartest venture firms: a16z spent 2025 building a full in-house media and events operation — dinner series, IRL fellowships, 40,000-person city-wide tech festivals — with the explicit argument that high-trust, in-person environments become more scarce and more valuable as AI floods digital channels with synthetic noise. Lightspeed just hired its first-ever Partner & New Media Lead and has been running monthly IRL Generative Series events across SF, LA, NYC, and Europe as community infrastructure for AI builders. H&F is running the same trade in edtech: own the room where real relationships form. If you’re building products or professional development for educators, the live event layer is your distribution infrastructure — and it just got a serious upgrade.

AI pricing is moving from flat-rate to metered and schools need to think about what that means for their budgets

The AI labs are quietly shifting from subscription bundles toward usage-based pricing, especially for coding and agentic tools. Azeem Azhar’s Exponential View had the clearest breakdown this week: flat-rate bundles work when marginal costs are low, but agents change the math with power users burning billions of tokens in a month. The education implication isn’t that AI will become unaffordable. It’s that institutions that haven’t built internal governance around AI usage are about to get sticker shock at renewal time — and districts that purchased flat-rate site licenses without any usage data are flying blind. This connects directly to the CSU contract renewal story above: usage data and defined outcomes are going to be the two non-negotiables in every enterprise AI contract from here on out.

The trust breakdown between faculty and students is the least-covered story with the most downstream consequences

EDUCAUSE flagged it this week and it keeps coming up in conversations: AI is changing the trust dynamic between faculty and students in ways that policy can’t fully address. It’s not just about academic integrity. It’s that faculty who rely on written work to understand what students know are losing confidence in that signal. What Horn’s testimony names as “AI accelerating the processes and priorities currently in action” is most visible here. The institutions moving fastest on oral defenses, portfolio-based assessment, and demonstration-of-learning models aren’t just solving for cheating, they’re rebuilding the epistemic relationship between teachers and students.

💰 Funding & market moves

OpenAI launches OpenAI for Singapore (announced in May but flagging it here if you missed it) — S$300M+ government partnership anchoring its first Applied AI Lab outside the US, including workforce and education programming.

This is more than a flag in the ground. The Applied AI Lab will create 200+ Singapore-based technical roles, a Singapore chapter of the OpenAI Academy for educators, Codex for Teachers hackathons, and a Forward-Deployed Engineer training program to build local AI deployment talent. The Ministry of Education is a named partner. So this is a national education strategy, not just a tech deal.

Last week, I wrote about OpenAI going country-direct and what it means for who the edtech competition even is — Singapore now joins Armenia, Jordan, Greece, Kazakhstan, Slovakia, and Estonia in OpenAI’s Education for Countries program. The race is moving fast and it’s not just OpenAI: Google + UNICEF deployed in Brazil, India, Pakistan, and Kenya that same week. The countries building this infrastructure now are the ones US students will be competing with in 10 years and they are not waiting.

I’m working on a longer piece on the three-way divergence between OpenAI’s top-down national reach, Google’s friction-free access through Workspace, and Anthropic’s teacher-as-co-architect model — more soon.

📚 Research & policy on my radar

“4 Questions We Must Answer Before Bringing AI Into the Classroom” — EdWeek Tech Leader The four: Does it improve student learning? Does it reduce equity gaps? Does it support teacher autonomy? Does it build long-term capacity? Simple framework, but the sequencing matters — student learning first, everything else second. Good scaffolding for school leaders who need something beyond “does the vendor have a good demo.”

“The AI High School is Surprisingly Human” — NYT Opinion The first dedicated “AI high school” looks mostly like... school. Human teachers, messy learning, genuine relationships. The AI is in the infrastructure and curriculum framing, not replacing the human moments. I keep sending this to school leaders who need permission to move forward without abandoning what actually works.

That’s The Syllabus for this week. Lots to dig into!

If this was useful:

📬 Subscribe on Substack — The Syllabus will land in your inbox every Monday. I also write there on an ad hoc basis when something in the field deserves a longer take.

🔗 Connect with me on LinkedIn — I post on AI in education, edtech market signals, and what I’m seeing on the ground from my advisory work.

If you’re working on something in this space:

I advise school districts building AI frameworks and edtech founders navigating the K-12 market. If you’re doing either of those things and want a thought partner, reach out.

If you know someone who should be reading this:

Forward it. Re-stack it. Share it on Linkedin. The best readers I’ll ever have are probably in someone else’s inbox right now.

Hi, I’m Angela Chen! I’m an edtech founder, Stanford instructor, and AI in education advisor. Every week, I read across the full AI-in-education landscape: research, classroom practice, startup and funding signals, policy, and market intelligence.

I’ve been doing this privately for my own work and a few people kept asking what I was tracking. So I’m sharing the signals I actually think matter, synthesized across everything I read that week, publically now!

If there’s something here worth going deeper on, comment below or DM me on LinkedIn and tell me which signal — I’m always interested in what’s actually landing for people in the field.

The mood this week is bifurcated: on one hand, AI is quietly becoming infrastructure — districts building their own tools, nations signing direct deals with OpenAI, universities positioning as innovation hubs. On the other hand, the guardrails conversation is getting louder and more organized, with teachers’ unions, researchers, and practitioners all pushing back on “AI confidence” that doesn’t translate to competence.

⚡ What to pay attention to right now

Districts are becoming their own edtech vendors and that’s a bigger deal than it sounds

An Oregon school district is projecting $200K in savings by using AI-powered “vibe coding” to build custom instructional tools in-house, according to EdWeek’s reporting from May 29. This isn’t an isolated story, it’s an early signal of a structural shift. When AI makes coding accessible enough that district IT teams can build custom solutions faster and cheaper than procuring from vendors, the traditional edtech sales model starts to crack.

For vendors, the response has to be products that are genuinely harder to replicate by district IT teams: deep integrations, compliance infrastructure, and curriculum-grade content. For districts, the question is sustainability — who maintains these tools when the initial savings wear off? This is a trend worth watching closely.

OpenAI is going country-direct and it’s reshaping who the edtech competition even is

Armenia is the latest country to sign on with OpenAI’s Education for Countries program, a direct government-to-OpenAI partnership to embed AI into national education infrastructure. This follows similar moves in other countries (Jordan, Greece, Kahzakstan, Slovakia, Singapore, and Estonia) and represents something the traditional edtech ecosystem wasn’t built to compete with: foundation model companies offering subsidized, integrated AI capacity at the national level.

The US doesn't have an equivalent counterpart — education is a state function, fragmented by district, and there's no federal body that could sign a comparable deal. The deeper competitive picture is that OpenAI isn’t the only one moving here — it’s just moving most visibly. Google is winning on access by embedding Gemini directly into Workspace for Education tools millions of schools already use: no new procurement, no friction, already there. Anthropic is taking a different approach entirely — its Teach For All partnership puts teachers as co-architects of AI tools across 63 countries rather than recipients of them.

I’m writing a longer piece on what this three-way divergence actually means for the field. The short version: OpenAI is winning on top-down reach, Google on educator access, Anthropic on pedagogical intentionality — and which of those bets pays off will depend entirely on whether outcomes data ever catches up to deployment speed.

The American Federation of Teachers just drew a hard line and it’s going to shape district procurement decisions

The American Federation of Teachers released a comprehensive AI plan this week calling for complete screen bans for pre-K through grade 2, bans on student-facing AI tools across all of elementary education, and substantial teacher training requirements as a precondition for any AI tool adoption.

Whether or not you agree with the specific policy asks, the signal is clear: labor unions are now becoming a formal stakeholder in edtech procurement decisions, not just an afterthought. Districts that want smooth implementation need union buy-in, and union buy-in increasingly means credible PD infrastructure — not pilot programs and slide decks.

New research: student AI confidence is inversely correlated with AI competence

AI for Education’s May 28 newsletter flags a new study on middle schoolers showing that students who are most confident using AI tools actually perform worst — they can’t distinguish good AI outputs from bad ones and rarely ask follow-up questions. This is a classic “confidence trap” problem and it has direct implications for how we design AI literacy curricula. Familiarity with tools is not the same as understanding of their limitations. The practical implication for K-12: AI literacy programs that focus on tool access without building critical evaluation skills may actually widen the competency gap rather than close it.

MIT/Harvard data on AI and jobs: grounding the workforce narrative

MIT Technology Review’s June 1 issue digs into the actual Bureau of Labor Statistics data and Harvard economist David Deming’s longitudinal survey tracking AI adoption across the workforce and found that AI hasn’t had large-scale labor market impact yet. About 40% of workers use generative AI, productivity gains exist but aren’t economy-shaking, and unemployment in AI-exposed occupations is actually lower than in less-exposed ones.

This matters for how we talk to students, parents, and district leaders about workforce preparation. The honest answer right now is not “AI is killing jobs” but “AI is changing the skills that matter, and we don’t have great data yet on what comes next.”

🛠️ Tools of note

(These aren’t startup investment recommendations — they show up here because they’re connected to trends worth tracking.)

AI for Education’s 45-Hour Practitioner Course — “Generative AI in Your Practice: From Knowledge to Application” — a self-paced course for educators launching June 1

Why it’s trending: This is one of the most substantial educator AI literacy infrastructure I’ve seen built to date. The move from micro-credentials to a full 45-hour course reflects that surface-level PD is no longer sufficient — both practitioners and districts are demanding depth.

EDSAFE AI Policy Essentials Course — A free, 10-module self-paced course (~3.5 hours total) built specifically for district leaders: superintendents, school board members, CTOs, and policy committees.

Why it’s trending: It’s a direct response to the governance gap the AFT plan and CoSN data both flagged this week — most districts have acceptable use policies that weren’t built for generative AI, and leaders lack practical frameworks for procurement decisions, vendor accountability, and data privacy. This is the structured infrastructure for that. Worth sharing with any district leader you’re advising.

💰 Funding & market moves

HolonIQ’s $200M Partnership for Health, Education & Economic Mobility — HolonIQ flagged a new $200M cross-sector partnership connecting health, education, and economic mobility in their May 29 ecosystem hubs report. The broader HolonIQ report on Edtech Ecosystem Hubs is worth reading: it maps how universities are showing up at every stage of edtech innovation, from R&D through commercialization to adoption — and argues they’re underutilized as infrastructure for the sector.

📚 Research & policy on my radar

“Student AI Confidence Hides Real Gaps” — AI for Education (mentioned above)

A study tracking middle schoolers found that confidence with AI tools doesn’t translate to competence and may make things worse. Students with the most positive AI attitudes performed worst, couldn’t distinguish good from bad AI outputs, and rarely asked follow-up questions. This means K-12 AI literacy programs that emphasize access and familiarity over critical evaluation are potentially counterproductive — so build for for skepticism, not enthusiasm.

“AI Isn’t Killing Jobs (Yet)” — MIT Technology Review, June 1

The data from BLS and Harvard’s longitudinal survey shows AI adoption is broad but economic impact is modest and job displacement is not yet measurable at scale. The piece argues that workforce AI preparation should focus on judgment, adaptability, and critical AI use — not fear. The skills gap is real, but it’s not the apocalypse narrative, and teaching to panic helps no one.

“Governing Generative AI in Higher Education” (new publication) — AI Edu Simplified / Substack, May 29

A new collaborative publication on AI governance frameworks for higher ed. Governance decisions flow will downstream to K-12 too and the frameworks colleges adopt now for student AI use, academic integrity, and data privacy will shape what K-12 districts face in three to five years.

That’s The Syllabus for this week. Lot’s to dig into!

If this was useful:

📬 Subscribe on Substack — The Syllabus will land in your inbox every Monday. I also write there on an ad hoc basis when something in the field deserves a longer take.

🔗 Connect with me on LinkedIn — I post on AI in education, edtech market signals, and what I’m seeing on the ground from my advisory work.

If you’re working on something in this space:

I advise school districts building AI frameworks and edtech founders navigating the K-12 market. If you’re doing either of those things and want a thought partner, reach out.

If you know someone who should be reading this:

Forward it. Re-stack it. Share it on Linkedin. The best readers I’ll ever have are probably in someone else’s inbox right now.

Hi, I’m Angela Chen! I’m an edtech founder, Stanford instructor, and AI in education advisor. Every week, I read across the full AI-in-education landscape: research, classroom practice, startup and funding signals, policy, and market intelligence.

I’ve been doing this privately for my own work and a few people kept asking what I was tracking. So I’m sharing the signals I actually think matter, synthesized across everything I read that week, publically now!

If there’s something here worth going deeper on, comment below or DM me on LinkedIn and tell me which signal — I’m always interested in what’s actually landing for people in the field.

The mood this week: a field splitting in two. Massive philanthropic capital is flowing into AI-for-education infrastructure at the foundation layer, while on the ground, districts are cutting ed-tech tools, parents are petitioning to remove screens, and the federal government just told schools to go back to pencil and paper.

The tension between the builders and the resisters has never been sharper — and both sides have real data to work with.

⚡ What to pay attention to right now

Anthropic + Gates commit $200M to AI for good and it’s not hype money

A 4-year, $200M partnership between Anthropic and the Gates Foundation pairs Claude credits and Anthropic staff with Gates grant dollars, explicitly naming evidence-based tutoring, career guidance, and foundational literacy and numeracy as target areas. (Reuters) What makes this different from most philanthropic AI announcements is the specificity: this isn’t “explore AI in education” money — it’s going toward use cases that already have a research base.

What sets this apart from most AI-in-education funding announcements is the Gates Foundation’s involvement — they’ve spent 20 years stress-testing what actually moves outcomes in K-12, and they don’t attach their name to things without an evidence framework.

For builders: watch what use cases get resourced through this pipeline. They’re likely to set the evidence bar for the next major procurement cycle.

The Surgeon General’s office just officially entered the screen time debate and it cuts against ed-tech’s default assumptions

The Trump administration’s Surgeon General’s office formally advised schools to limit student screen time and emphasize paper-and-pencil assignments. (EdWeek) It landed right as Iowa passed a law limiting screen time and districts around the country are already cutting ed-tech tools under budget pressure.

For AI edtech builders, this is a real procurement headwind: even if your product has evidence, you’re now selling into a political environment where the default presumption is shifting toward less screen time, not more. The companies that survive this cycle will be the ones that can make a crisp case for which screen time generates learning gains, not just that they use AI.

K-12 device procurement is being disrupted by AI-driven demand, but not in the way you’d expect

SmartBrief flagged last week that K-12 device procurement is being actively disrupted by AI-driven demand shifts (SmartBrief). Districts are being asked to spec for AI workloads — more processing power, different connectivity requirements — at exactly the moment budgets are tightest and the federal screen time message is creating hesitation.

This creates a practical bind for edtech vendors: the infrastructure layer isn’t keeping up with the product layer, and the districts most excited about AI tools are often the ones least equipped to run them well.

Teachers are getting AI training at scale yet it’s having difficulty landing

Two data points from the same week: AI professional development for teachers is growing rapidly yet a new study finds teachers are still broadly wary of AI — with disparity risks named as a top concern (SmartBrief).

This is the central tension in K-12 AI implementation right now. Training is scaling but trust is not. The wariness about equity is well-founded. AI tools have documented disparities in performance across student populations. PD that doesn’t address this directly isn’t just ineffective, it’s counterproductive.

OpenAI’s realtime voice models might be about to reshape what AI tutoring feels like

OpenAI launched three new realtime voice API models last week — voice reasoning, live translation, and live transcription — explicitly aimed at developers building education products (OpenAI for Education). Students won’t use the API directly, but tutoring platforms, LMS tools, and accessibility products will.

The near-term unlock that’s most underserved: multilingual learner support. Live translation in parent-teacher conferences alone would be meaningful for districts with high ELL populations. Longer term, voice-first AI reduces the typing bottleneck that makes most current AI tutors feel clunky, enabling learning tools to feel more like conversations than forms are genuinely different products. Watch which companies move first to integrate.

I’m genuinely curious about how Anthropic responds. Claude is already the backbone of a number of edtech products and the Gates Foundation partnership announced mid May puts Anthropic squarely in the education market in a more intentional way than before. Does Anthropic’s education team ship something that competes directly on voice or do they double down on reasoning and written feedback, where Claude has a strong case? The answer will shape which model edtech builders reach for when voice becomes table stakes.

🛠️ Tools of note

(These aren’t investment recommendations, they show up here because they’re connected to trends worth tracking.)

Custom GPT suite for pre-calculus (Bhavani Kola / Eclatech Solutions) — Bhavani, a math department chair, built a suite of purpose-built GPTs wired directly into Canvas: syllabus formatting, LaTeX accessibility conversion, animated concept explainers via Manim, AI-resistant assessment design, and interactive flashcard generators.

Why it’s trending: This is practitioner-built AI infrastructure, not vendor-sold. It directly answers the “AI just does the homework” critique by centering accessibility and AI-resistant assessment design. It’s also a preview of what teacher-as-builder looks like at scale, which is relevant for anyone thinking about professional development models.

Indiana University GenAI 101 — Free, open-enrollment foundational AI literacy course; 114,000+ enrolled worldwide as of this week.

Why it’s trending: Governments and universities racing to provide free AI literacy are making the “we can’t afford AI training” argument increasingly difficult to sustain. This is the public-good track of AI skills education going at genuine scale, and it benchmarks what districts could point students toward before buying a vendor solution.

💰 Funding & market moves

Multiverse — Apprenticeships-to-enterprise-AI-training platform, UK $70M raise at $2.1B valuation (a $400M step-up from 2022), led by Schroders Capital with General Catalyst, Lightspeed, Index Ventures. Revenue up 50% YoY (The EdSheet).

This is the second edtech unicorn after Handshake to explicitly pivot from its original model toward “AI adoption platform” positioning. Revenue growth suggests the pivot is real, not just a rebrand.

What it signals for the broader market: enterprise AI training is where the growth story is right now and companies with existing employer relationships are moving fastest to own that position.

Kalos Ventures Fund I — Early-stage workforce, care, and education $78.8M first close, oversubscribed. Early portfolio includes Manifest (SMB-focused AI learning platform) and Rosarium Health.

A fund explicitly targeting the non-enterprise, non-higher-ed quadrant of AI training — small businesses, care workers, frontline employees. This is the part of the labor market that large platform consolidations (Coursera-Udemy, upGrad-Unacademy) are least likely to serve well.

Presence acquires Education Modified — Special education workflow, US Teletherapy and evaluations platform Presence expands into IEP and special education workflow management across 10,000+ schools in 47 states.

Special ed is the most compliance-heavy, documentation-intensive corner of K-12 and AI workflow tools here have a genuinely clear ROI story for districts that are simultaneously under budget pressure and facing rising special ed caseloads.

📚 Research & policy on my radar

Workforce Pell Grant final rule is now in effect — HolonIQ/QS, May 18

The US Department of Education’s final rule expanding Pell Grant eligibility to short-term workforce credentials took effect May 18.

For K-12 AI builders, this matters because it directly funds the career pathways pipeline that secondary schools are supposed to feed. More credential-eligible training options strengthen the case for high school AI and tech programs that connect to community college articulation and it gives districts a funding hook for programs they couldn’t previously justify.

CoSN: Security and AI guidelines are the #1 and #2 ed-tech concerns for districts — EdWeek Market Brief, May 25

CoSN’s annual survey of district technology leaders puts data security and AI policy development ahead of budget or infrastructure as leading concerns.

The practical implication for vendors: if your AI tool doesn’t come with credible data governance and a policy framework out of the box, you’re asking IT directors to say yes to something that immediately becomes their problem to solve. Showing up to a district RFP without a clear data handling answer is no longer a minor gap — it’s a deal-breaker.

PowerSchool’s new CEO signals data + automation as K-12’s next era — EdWeek Market Brief, May 25

Antonio Pietri, seven months in, is framing PowerSchool’s direction around data infrastructure and automation. (EdWeek Market Brief) PowerSchool sits on more K-12 student data than almost any other company. If it moves aggressively into AI-driven insights and automation on top of that data layer, that’s a significant competitive shift for the assessment, intervention, and analytics tools that currently sell alongside SIS platforms.

That’s The Syllabus for this week.

Lots to dig into! I’m especially curious whether others are seeing the grade-differentiated device policy conversation taking shape in their districts.

This is an experiment. I’ve been doing this research privately for years — tracking signals across the AI in education ecosystem so I can show up sharper for my advisory clients and my Stanford students. A few people kept asking what I was reading. So now I’m sharing it. If it’s useful, tell me. If something’s missing, tell me that too.

If this was useful:

📬 Subscribe on Substack — The Syllabus will land in your inbox every Monday. I also write there on an ad hoc basis when something in the field deserves a longer take.

🔗 Connect with me on LinkedIn — I post on AI in education, edtech market signals, and what I’m seeing on the ground from my advisory work.

If you’re working on something in this space:

I advise school districts building AI frameworks and edtech founders navigating the K-12 market. If you’re doing either of those things and want a thought partner, reach out.

If you know someone who should be reading this:

Forward it. Re-stack it. Share it on Linkedin. The best readers I’ll ever have are probably in someone else’s inbox right now.

Hi, I’m Angela Chen! I’m an edtech founder, Stanford instructor, and AI in education advisor. Every week, I read across the full AI-in-education landscape: research, classroom practice, startup and funding signals, policy, and market intelligence.

I’ve been doing this privately for my own work and a few people kept asking what I was tracking. So I’m sharing the signals I actually think matter, synthesized across everything I read that week, publically now!

If there’s something here worth going deeper on, comment below or DM me on LinkedIn and tell me which signal — I’m always interested in what’s actually landing for people in the field.

The mood this week is bifurcated: record capital flowing into edtech operations and university AI institutes, while the practitioner layer is wrestling with two hard questions — 1) what do we do with agentic AI in classrooms and 2) are we about to kneecap device access right when students need it most?

⚡ What to pay attention to right now

The K-12 edtech+ market is turning a corner and operations software is where growth investors are placing their bets

Two major rounds landed within weeks of each other: Minga’s $65M from Riverwood Capital (student behavior and engagement — think digital hall passes, student IDs, attendance) and Zum’s $100M from TPG for K-12 transportation. Neither is an AI-first — or even learning-first — play. Both are infrastructure, the kind of operational backbone that districts depend on regardless of budget cycles.

EdWeek Market Brief’s State of the K-12 Industry 2026 report adds the framing: revenue optimism is up for the first time in three years. Growth equity has concluded that districts are stable enough to invest in, but they’re picking durable, recurring-revenue operational tools over speculative AI curriculum plays. For edtech+ founders: if your product is a must-have workflow rather than a nice-to-have AI feature, the funding environment just got meaningfully better. That said, the early-stage market remains quiet. This is a story about growth and private equity, not seed rounds.

Stanford just merged HAI and its Data Science institute and the combined entity has an explicit K-12-through-lifelong-learning mandate

Stanford merged its human-centered AI institute and its data science initiative into a single, expanded organization this month. James Landay will lead it and Fei-Fei Li moves into a university-wide Special Advisor on AI role. The combined institute brings together 400+ scholars, a $60M cumulative grant portfolio, and significant computing infrastructure — and has organized its work around three pillars, one of which is explicitly “transforming education from K-12 through lifelong learners.”

This matters beyond Stanford. When the institution that sets the agenda for AI research also declares K-12-to-lifelong-learning a core pillar, it signals where the serious research money and talent will flow. The Stanford Accelerator for Learning is already running a new seed grant program — AI in Teaching and Learning at Stanford — with proposals due this Thursday, May 15. Worth knowing about if you have Stanford-affiliated collaborators.

The screen backlash is colliding head-on with AI literacy mandates and the contradiction is a real problem

Los Angeles Unified is reviewing all edtech contracts as part of a push to limit student screen time. Meanwhile, New York City, Chicago, and Denver have all issued guidance designating AI lesson planning and drafting as green lit uses. Research on cellphone bans shows mixed academic achievement results.

A piece worth reading carefully this week is this careful unpacking from a veteran teacher: K-8 device rollbacks are defensible and overdue, but blanket bans through 12th grade would dismantle the AI literacy infrastructure states have spent two years building. His argument that resonates with me: the K-8 case and the 9-12 case are not the same question, and treating them as one is where policy goes wrong. District decision-makers need a grade-differentiated framework, not a binary.

I’m wrestling with this exact challenge in a current engagement with a K-5 private school — what’s the right balance for AI implementation school-wide? How early might be too early to start introducing AI literacy concepts to children? I’m currently halfway through this engagement — looking forward to sharing thoughts after I wrap up my work with them later this month. If you’re leading a school and looking for support with AI policy and implementation — reach out! I’d love to help you through this tricky challenge.

Agentic AI is arriving in education and the field doesn’t have a good answer yet on what “learning” means when friction disappears

A practitioner post from AI+Edu=Simplified documents real agentic AI experiments — organizing 1,200 research PDFs, building custom apps on a one-hour train commute, chunking video by timestamp — and lands on the right hard question: agentic AI is optimized to remove friction, but learning often depends on intentional friction. Deliberation, struggle, and the productive discomfort of not-yet-knowing are features, not bugs.

What does it look like when students can delegate organizational and contextual tasks to an agent and spend more cognitive bandwidth on actual learning? What happens when they delegate the learning itself? The field doesn’t have confident answers yet. I think this conversation needs to happen as AI policy decisions are made.

The Virtual Learning Academy Charter School’s mastery-based model looks prescient in the age of AI and their evolution is worth watching

Michael Horn’s interview with outgoing co-founder Steve Kossakoski and new CEO Natalie Berger is a long read worth your time. The Virtual Learning Academy Charter School (a New Hampshire competency-based virtual charter) only gets funded when students demonstrate mastery — literally a payment-for-progress model baked into their charter. In a world where AI can complete traditional coursework, their discussion-based assessment model — 1:1 oral conversations at the end of every competency — becomes a natural integrity layer. Their exploration of using AI as a co-discussant in those assessments is exactly the kind of practitioner-led experimentation the field needs more of.

For K-12 advisors: this school is a model worth holding up when districts ask “how do we ensure students actually learned something?”

🛠️ Tools of note

(These aren’t startup investment recommendations, they show up here because they’re connected to trends worth tracking.)

Teacher’s Buddy — AI platform that generates differentiated lesson plans, assessments, and reports aligned to local curricula; raised $2.3M and reached 12,000+ teachers across 130 countries in 18 months.

Why it’s trending: The localization piece — curricula alignment to specific national standards — is what the US market has mostly failed to crack at scale. That growth pace pre-raise is a signal about global demand running ahead of US-centric product development.

Blomma — $25/month AI career coach built around structured goals and contextual inputs (calendars, performance reviews, resumes); raised $5M from Felicis, aimed at Gen Z workers who lost informal office mentorship post-COVID.

Why it’s trending: Blomma points to something I’ve thought about since my own first edtech company → AI learning products aimed at the 18-to-30 young adult demographic may find fundamentally easier test cases than K-12. That cohort controls its own purchasing decisions, has immediate and legible career stakes, doesn’t require parental consent or district procurement, and is already accustomed to consumer subscription tools. The informal mentorship gap left by remote work is real and large. This is where a lot of AI-in-learning innovation is likely to move first before it works its way back into formal education — and it’s worth watching as both a market signal and a product laboratory.

📚 Research & policy on my radar

AI adoption barriers persist even as the market stabilizes — EdWeek Market Brief, State of the K-12 Industry 2026

Revenue expectations are up for the first time in three years, but districts still cite uncertainty, procurement friction, and unclear outcomes as barriers to AI adoption. Edtech companies pitching AI to districts need sharper evidence stories, not just feature lists.

Cellphone bans show mixed results and the research is still young — EdWeek, May 8

Researchers recommend continued study. The honest answer for K-12 advisors: resist citing “bans work” or “bans don’t work” with confidence. Grade-level differentiation almost certainly matters more than blanket policy, and the data to support that case is still being gathered.

The Canvas cyberattack exposed the single-point-of-failure risk of edtech consolidation — NYT, May 7 / Wired

A ransomware attack hit Instructure (Canvas), disrupting thousands of colleges during finals week and exposing 275 million people’s data. This is the worst-case scenario from a single-platform dependency — and it opens a real conversation with districts about edtech stack concentration risk and vendor data governance.

MIT Technology Review names this “the era of AI malaise” — MIT Technology Review, May 8

The public is neither rejecting AI nor embracing it — just sitting uncomfortably with uncertainty about what it will do to work and society. I see the same thing in district leaders. The framing is useful: name the malaise, then give people a concrete first step rather than another framework.

That’s The Syllabus for this week.

Lots to dig into! I’m especially curious whether others are seeing the grade-differentiated device policy conversation taking shape in their districts.

This is an experiment. I’ve been doing this research privately for years — tracking signals across the AI in education ecosystem so I can show up sharper for my advisory clients and my Stanford students. A few people kept asking what I was reading. So now I’m sharing it. If it’s useful, tell me. If something’s missing, tell me that too.

If this was useful:

📬 Subscribe on Substack — The Syllabus will land in your inbox every Monday. I also write there on an ad hoc basis when something in the field deserves a longer take.

🔗 Connect with me on LinkedIn — I post on AI in education, edtech market signals, and what I’m seeing on the ground from my advisory work.

If you’re working on something in this space:

I advise school districts building AI frameworks and edtech founders navigating the K-12 market. If you’re doing either of those things and want a thought partner, reach out.

If you know someone who should be reading this:

Forward it. The best readers I’ll ever have are probably in someone else’s inbox right now.

Hi, I’m Angela Chen! I’m an edtech founder, Stanford instructor, and AI in education advisor. Every week, I read across the full AI-in-education landscape: research, classroom practice, startup and funding signals, policy, and market intelligence.

I’ve been doing this privately for my own work and a few people kept asking what I was tracking. So I’m sharing the signals I actually think matter, synthesized across everything I read that week, publically now!

If there’s something here worth going deeper on, comment below or DM me on LinkedIn and tell me which signal — I’m always interested in what’s actually landing for people in the field.

A lot converged this week around a single tension: the institutions aren’t keeping up with the tools, and that gap is where most of the risk — and most of the opportunity — lives right now.

⚡ What to pay attention to right now

The K-12 edtech market is stabilizing, but the buyer mindset hasn’t caught up yet.

The new State of the K-12 Industry 2026 report from EdWeek Market Brief is the clearest snapshot of where the market actually is right now. Revenue declines have deepened — but revenue expectations are rising for the first time in three years. That’s a meaningful shift.

At the same time, the report flags that AI adoption is evolving but the biggest barriers are still coming from districts themselves: uncertainty, procurement friction, and a lack of frameworks for evaluation.

Texas gets called out specifically: enrollment losses, tighter budgets, and policy volatility are making it one of the hardest markets for vendors despite its size. The signal for anyone building or selling into K-12 — the technology is ready, the institutions aren’t, and the design constraint is the institution, not the product.

The parent tech backlash is real and “ban it all” is the wrong response.

Multiple sources converged on this this week. EdWeek’s piece “Why Schools Need Tech. And Why They Don’t” captured the split in K-12 sentiment clearly. SmartBrief on EdTech led with parents pushing for — and getting — device limits, with legislators closely following behind.

But the nuance that keeps getting lost in these conversations is the backlash is all about consumer tech — phones, passive scrolling, TikTok. It’s not, or shouldn’t be, about purpose-built tools that support learning or reduce teacher workload.

Conflating them is a policy mistake, and a lot of districts are making it right now. For anyone advising schools: help them build that distinction into their policy language before a broad ban flattens it.

Frameworks for AI decision-making in schools are finally maturing, and two landed this week.

AI for Education released a free “Framework in Action” resource built on their SEE Framework (Safe, Ethical, Effective). It gives educators a structured process for any AI scenario — classroom use, tool procurement, academic integrity policy — and it travels: it works for a teacher, a department head, or a school board.

At the same time, AI+Edu=Simplified published a rich reflection on how scholars are figuring out their personal “line” on AI use. His workshop finding is worth sitting with: 310 participants across five categories landed on one shared instinct — AI should refine thinking, not originate it.

GPT-5.5 leads benchmarks but hallucinates more — and the difference matters enormously in education.

DeepLearning.AI had the clearest breakdown this week of the GPT-5.5 launch. It tops the Artificial Analysis Intelligence Index, but its hallucination rate is 85.5%, compared to Claude Opus 4.7 at 36% and Gemini 3.1 at 50%. It also lied about completing impossible tasks in 29% of tested samples, up from 7% in the previous version.

In educational contexts, hallucination isn’t just an inconvenience — it’s a trust and safety issue. When a student gets confident wrong information, the downstream effects (bad research, eroded critical thinking, liability exposure for schools) are real.

Anyone making AI tool recommendations for districts should have hallucination rate data in hand, not just benchmark rankings. Also worth noting: this is the fourth flagship model launch since February across the major labs. Any school district “approved tools” list is outdated by the time it’s approved!

AI literacy just went mainstream and the classroom gap is about to get wider.

Andrew Ng announced AI Prompting for Everyone, a new DeepLearning.AI course aimed at non-technical general audiences. It covers deep research mode, multi-document context, agentic workflows, and practical skills like data analysis and simple app building.

This is yet another clear signal yet that AI literacy is moving from early adopters to general population — which means the gap between your most and least AI-literate students is about to widen faster than most classrooms are designed to handle. That’s a curriculum design challenge, not just a tech one.

🛠️ Tools of note

(These aren’t startup investment recommendations, they show up here because they’re connected to trends worth tracking!)

Talkido — screen-free platform for social and language skill development through interactive learning, featured in the EALA Tech Tool Library this week.

Why it’s trending: screen-free edtech is a direct response to the tech-limit legislation moment. There’s a growing market for tools that deliver on learning outcomes without adding to the device-time debate.

KickUp — connects educators with relevant professional development, surfaced in SmartBrief this week.

Why it’s trending: PD personalization is one of the highest-leverage levers for scaling teacher AI readiness at a district level, and it’s chronically underinvested. Tools that match teachers to PD based on actual need (vs. one-size mandates) are in the right place at the right time.

💰 Funding & market moves

Literati — children’s book subscriptions, now expanding into school book fairs with new PE backing. A bet on the physical, curated, school-facing product layer at a moment when everything is supposedly going digital. An interesting counterpoint and a direct play on Scholastic’s dominance in a space that hasn’t seen real competition in decades.

StudyPoint — test prep and tutoring company, acquired (acquirer unnamed in this week’s reporting via EdWeek Market Brief). The pattern is consistent: AI-native tutoring companies are putting pressure on legacy human-tutor models, and that pressure is accelerating consolidation. This space is going to look very different in 18 months.

📚 Research & policy on my radar

Disability-inclusive AI policy is becoming a formal advocacy front — EALA + New America policy brief

EALA and New America released a new brief arguing that AI deployment in schools has outpaced civil rights protections for students with IEPs and 504s. There’s a companion webinar on May 8 if you want to go deeper.

Practical implication: IEP/504 compliance in AI tool procurement is becoming a real audit risk, and most districts aren’t ready for that conversation yet.

Text-to-speech in math: benefit or distraction? — Digital Promise

Dr. Xin Wei’s new analysis of NAEP Grade 8 math data shows real potential benefits for text-to-speech tools but also raises genuine questions about when accessibility tech supports learning versus when it becomes a cognitive shortcut. This is the nuanced conversation schools need to be having instead of defaulting to “accommodations = always good.”

Top-down AI mandates in schools are underperforming bottom-up adoption.

The pattern across multiple sources this week: institutions that give individual teachers and faculty agency in AI adoption see better outcomes than those mandating specific tools from above. Governance frameworks beat tool mandates, every time.

This has direct implications for how district AI policies get written and how edtech companies think about their go-to-market.

Academic research under political fire — Michael B. Horn / Future U.

Federal research funding threats (NIH cuts, broader anti-university posture in Washington) are creating real instability for ed research pipelines. The downstream effect: practitioner-led and privately funded research is going to fill the gap — and it’ll carry different incentives than publicly funded work. Worth being clear-eyed about what that means for which findings get amplified.

That’s The Syllabus for this week.

This is an experiment. I’ve been doing this research privately for years — tracking signals across the AI in education ecosystem so I can show up sharper for my advisory clients and my Stanford students. A few people kept asking what I was reading. So now I’m sharing it. If it’s useful, tell me. If something’s missing, tell me that too.

If this was useful:

📬 Subscribe on Substack — The Syllabus will land in your inbox every Monday. I also write there on an ad hoc basis when something in the field deserves a longer take.

🔗 Connect with me on LinkedIn — I post on AI in education, edtech market signals, and what I’m seeing on the ground from my advisory work.

If you’re working on something in this space:

I advise school districts building AI frameworks and edtech founders navigating the K-12 market. If you’re doing either of those things and want a thought partner, reach out.

If you know someone who should be reading this:

Forward it. The best readers I’ll ever have are probably in someone else’s inbox right now.