EdTech is Borrowing Zuckerberg's Playbook
The "No Evidence of Harm" Refrain is a Familiar Deflection
This article was originally published on The Digital Delusion. We thank Jared for allowing us to share it with our readers.
Last week, Mark Zuckerberg — founder and CEO of Meta — took the stand under oath for the first time in a criminal trial.
At one point, Zuckerberg was questioned about Meta’s use of beauty filters: digital effects that make users, including children, appear younger, fitter, and more conventionally attractive in photos and videos.
The prosecution referenced Meta’s own internal review, Project MYST. According to reports, 18 out of 18 wellbeing experts who evaluated the psychological impact of these filters raised serious concerns about potential harm to young users’ mental health.
Despite those warnings, the filters remained available.
Zuckerberg’s defense rested on a familiar line of reasoning: there was no peer-reviewed, causal evidence demonstrating this specific product directly harmed children. Absent validated proof of causation, harm could not be established.
“There is no evidence of harm.”
This is the same argument now being deployed by EdTech lobbyists at statehouses across the country as lawmakers attempt to regulate classroom technology.
No Evidence of Causative Harm
This year, more than a dozen bills aimed at regulating EdTech have been introduced across at least nine states. Utah’s SAFE and BALANCE acts led the way, followed closely by Vermont’s effort to formalize parental opt-out rights and Tennessee’s proposal to remove digital devices from primary classrooms.
These efforts are informed by decades of research showing that, on average, classroom technologies do not outperform – and often underperform - well-implemented analog instruction.
Despite strong bipartisan support in most states, pro-tech lobbyists are pushing back with a familiar refrain: “There is no evidence of harm for emerging EdTech products.”
Strictly speaking, that statement is often true.
Educational technology evolves so rapidly that by the time researchers evaluate one platform, it has already been patched, rebranded, or replaced. Product-specific causal evidence is perpetually just out of reach.
But this is not a scientific defense. It is a misleading procedural maneuver.
When Causation Becomes Dangerous
Demanding product-specific, long-term, high-risk causative trials in children sets an unrealistic and ethically impossible standard.
Returning to beauty filters, no ethics board would approve a study deliberately exposing children to a tool that 18 experts consider risky simply to “prove” harm. That is why no randomized control trial has tested whether these filters damage young users’ mental health — the likely harms of such a study outweigh any possible benefits.
Luckily, we don’t live in a vacuum.
A substantial body of correlational research links image manipulation and filter use to body dissatisfaction, self-objectification, weight concerns, and reduced wellbeing. The experts reviewing Meta’s policies were not guessing — they were applying decades of psychological research to a new technological wrapper.
Software changes. Human biology does not.
The same logic governs learning.
Returning to Utah
Utah’s digital inflection year occurred in 2014, corresponding with the statewide launch of SAGE — a fully computerized adaptive assessment system. Before this, digital tools were largely peripheral in Utah classrooms. After this, they became structurally embedded.
Before widespread digital adoption, Utah NAEP scores rose consistently from 1992 through 2013. Pooled by subject and indexed to 2013:
Math scores increased +0.76 points per year
Reading scores increased +0.14 points per year.
After 2014, the slopes reversed:
Math scores declined -0.39 points per year
Reading scores declined -0.88 points per year.
This represents a structural swing of -1.15 points per year in math, and -1.02 points per year in reading. Importantly, excluding 2022 — the year most impacted by COVID-related closures — leaves these swings essentially unchanged: -1.05 points per year in math and -1.07 points per year in reading. In other words, this pattern is not a lockdown artifact — it’s a structural break beginning in 2015.
These are correlational patterns, but so were the early signals about smoking, lead exposure, and beauty filters.
When consistent patterns appear across nearly all 50 states’ NAEP data and across dozens of countries’ PISA, TIMSS, and PIRLS results — and when those patterns align with established cognitive mechanisms — we are no longer looking at coincidence. We are looking at converging evidence.
And what we cannot ethically do (just as with beauty filters) is deliberately expose children to systems we have strong reason to believe may undermine learning simply to satisfy an unrealistic evidentiary demand.
Demanding perfect causation before action doesn’t protect children; it protects developers.
A Generous Interpretation
Even if we assume the decline argument is overstated — that Utah’s NAEP data has merely “plateaued” since 2014 — the harm does not disappear.
Between 2015 and 2025, Utah invested roughly $500 million in K-12 educational technology. If half a billion dollars produces stagnation, that’s not neutral.
Every dollar committed to devices and platforms is a dollar not spent on interventions we know improve learning: teacher development, structured literacy programs, small-group instruction, targeted support for struggling students.
Even under the most generous interpretation of the data, the opportunity cost alone is staggering.
So Now Then…
Demanding definitive causative proof of harm before acting to protect children sets an unrealistic and dangerous standard. If we wait for perfect causation, we will always act too late.
Our society does not demand product-specific randomized trials before regulating food additives, vehicle safety standards, or consumer protections. We act when converging evidence suggests that risk outweighs benefit.
Education should be no different.
When billions of dollars and millions of children are involved, the burden of proof should rest on demonstrating clear, durable, replicable benefit — not on proving harm after the fact.
Caution is not fear, and restraint is not regression. They are marks of a society that prioritizes children over products.
For more on EdTech, check out Jared’s previous After Babel piece, The EdTech Revolution Has Failed.
 | A guest post by
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As a retired teacher, I feel I can attest to who benefits from a technology driven education. The teachers and the administration are under pressure to constantly provide progress data. When students' work is primarily on their computers, that can be achieved. It doesn't benefit the student but it reduces the workload of faculty. Education has devolved into statistical data and is no longer about creating a classroom of lifelong learners. It resembles an assembly line with machines creating identical models, only these are people, not automobiles.
The article is correct about one point: “no product-specific causal proof of harm” can serve as a convenient corporate standard when products evolve faster than research cycles. The problem arises in what it does next. It uses that procedural point to justify a substantive leap: from a specific type of social media risk (appearance manipulation, social comparison, engagement incentives) to a broad, catch-all category called “EdTech,” and then it treats that category as if it’s one exposure with a single mechanism.
“EdTech” is not a single entity. It encompasses calculators, audiobooks, text-to-speech, classroom projectors, learning management systems, adaptive tutoring, and high-stakes online testing. Some of these ease the learning process; some replace learning; some are neutral; and some can be harmful depending on age, usage, and what they substitute. If calculators are considered “technology in classrooms” (which they are), then the argument cannot simply be that “technology is damaging.” The real argument is that certain technologies, when used at developmental stages, in specific ways, can produce trade-offs. This is a different claim—one that needs definition and clarity.
The Utah NAEP story is presented as a “structural break” beginning around the introduction of a computerised assessment system. Even if the trendline description is accurate, the causal suggestion is not justified. A correlation between a statewide testing shift and later NAEP slopes does not identify the mechanism, and it does not distinguish school technology from the many other simultaneous changes that plausibly affect reading and maths: smartphones and social media saturation outside school, changes in reading habits, curriculum and standards adjustments, demographic change, teacher labour market dynamics, pandemic aftershocks beyond a single test year, and simple regression-to-the-mean after decades of improvement. If the claim is “classroom devices and platforms caused the decline,” you need evidence of exposure (time-on-device), substitution (what instruction was displaced), and a plausible pathway (attention fragmentation, reduced reading volume, reduced handwriting, weaker teacher-led instruction), ideally tested across multiple jurisdictions with appropriate controls. Without that, “converging evidence” is a rhetorical label applied to an under-specified hypothesis.
The ethics argument is also being used too broadly. It’s true that you often cannot run long, high-risk randomised trials in children. But that doesn’t justify collapsing categories and regulating by insinuation. Ethical limits should push policymakers toward better observational designs, clearer definitions, transparent procurement standards, and staged rollouts with measurable outcomes—not toward treating “digital” as the hazard.
If the author wants a defensible policy stance, it’s available without overreach. Shift the responsibility to vendors and districts to demonstrate durable, replicable benefits for instructional platforms before scaling. Require evaluations that measure not only short-term engagement but also learning transfer and retention. Limit entertainment-style features in student-facing products. Age-gate and minimise devices in early primary education where foundational skills are being automated internally. Protect time for reading, handwriting, and sustained attention. Maintain proven “utility tech” (calculators at the appropriate stage, accessibility tools like text-to-speech, assistive communication) precisely because the goal is learning, not a war on tools.
The article’s strongest point is opportunity cost. If districts spend heavily on platforms that don’t outperform well-implemented analogue instruction, that’s a serious policy failure. But “we wasted money” is not the same claim as “technology is damaging,” and it shouldn’t be conflated into one. A serious debate about schooling needs to discuss specifics: which tool, which age group, which dosage, what is being replaced, how it is measured, and compared to what alternative.