This is not a character flaw in a generation. It is a trained response. Every consumer app a student touches is engineered to interrupt — to pull attention away from whatever it was on and toward the next notification, the next video, the next refresh. School now competes with that engineering for the same scarce resource, and too often school has simply adopted the same tools and called it engagement. We have spent years measuring whether students are on their devices. We have spent almost no time measuring what those devices take from them while they are.

Intention asks why this tool, here, now? Investigation asks what information skills are in play? Implementation asks how are students engaging? Integrity asks how do we verify and attribute? The fifth and final pillar — Impact — asks the question that closes the loop: What did this cost or add?

Every other pillar of the Forward Lens looks at the front end of a tech-integrated lesson — the purpose, the skills, the engagement, the attribution. Impact looks at the back end. After the lesson is done, what is left? Did students learn more, deeper, in a way they couldn’t have learned otherwise — or did the lesson use up an hour of their attention without earning it?

The hardest part of Impact is not the asking. It is that the cost of attention is invisible until you measure it. A consumption-heavy lesson does not feel costly in the moment. It feels normal. It feels like school. Only when you start counting what was traded — minutes of sustained focus, opportunities for peer interaction, time spent making something rather than watching something — does the cost become visible.

The single most important body of research on attention in the digital age comes from Gloria Mark, Chancellor’s Professor of Informatics at UC Irvine. Mark’s team has been measuring people’s screen-attention spans since 2004. In 2004, the average person spent 2.5 minutes on any single screen before switching. By 2012, that number had dropped to about 75 seconds. By 2021, it was 47 seconds.

Forty-seven seconds. That is the cognitive environment today’s students live inside.

Mark’s other findings are equally relevant: it takes roughly 25 minutes to bring attention back to a task after an interruption, and people interrupt themselves more often than they are interrupted by others. The implication for classroom design is direct. Every notification, every tab switch, every quick AI query is not just a small cost — it triggers a refocus cycle that may not complete before the next interruption arrives. The tax compounds.

The RAND September 2025 report on AI in U.S. schools surfaces another impact concern: 61% of parents and over half of students believe AI use harms critical-thinking skills, even as 54% of students continue using AI for schoolwork. This is the cognitive cost showing up in self-report data, before researchers have had time to design rigorous measurement.

The well-being side of the impact picture is similarly clear. Common Sense Media’s research on adolescent screen time has documented sustained increases in daily screen exposure since 2020. Time on screens is now competing not only with classroom learning, but with sleep, physical activity, in-person peer interaction, and the kinds of unstructured cognitive downtime that adolescent brains actually need.

This is not an argument for removing technology from classrooms. It is an argument for making the cost visible — to teachers, to students, and to families — so that the decision to use technology in a lesson is a decision made with both sides of the ledger in view.

The most significant policy response to the attentional cost question has been the wave of phone-restriction laws that began in 2023 and accelerated through 2025.

Florida passed the Phone-Free Schools Act in 2023, becoming the first U.S. state to restrict student cell phone use during instructional time. California, Indiana, Louisiana, Minnesota, Ohio, South Carolina, and Virginia followed with similar legislation in 2024.

New York went farthest. As of the 2025-26 school year, New York is the largest state in the nation to require a statewide bell-to-bell smartphone restriction in K-12 schools — meaning phones are off and out of sight for the entire school day, including lunch and passing periods. Over 1,050 districts and schools had published their compliance policies by August 2025. Early reporting from districts implementing the law has described significant changes to classroom climate — increased peer interaction, less anxiety, more sustained focus.

Internationally, Australia rolled out national smartphone restrictions in schools beginning in 2024, with states phasing in bell-to-bell policies. The UK issued non-statutory guidance in 2024 encouraging schools to prohibit phones during the school day. France banned phones in primary and middle schools in 2018.

At the family level, the Wait Until 8th movement and the Phone-Free Schools Movement have organized parents and educators around the same principle: the cost of unrestricted phone access during the school day is no longer disputed, and the policy direction is consistent.

The school-issued device question is separate, and harder. But the policy direction on personal devices is now clear: the attentional cost has been recognized at the level of state and national law.

The most direct UDL 3.0 mapping for Impact is Engagement — specifically the sub-dimension of self-regulation. UDL 3.0 explicitly emphasizes “purposeful and reflective” learners. Self-regulation is the capacity students need to evaluate, in the moment, whether a tool is serving their learning or competing with it. That capacity is not innate. It is built through deliberate practice — the same way any metacognitive skill is built. Impact is the pillar that creates the practice. Asking, after every tech-integrated lesson, what did this cost or add? is the rep that builds the muscle.

This matters most for the students who will spend the longest time inside the attention economy. Teaching students to evaluate the cost of their own technology use is not a digital wellness add-on. It is one of the most important academic skills we can teach in 2026.

The team practice this week

A structured end-of-unit reflection for teachers and students. A “cost / add” ledger for the unit’s tools. A team-level conversation about what to keep, what to cut, and what to redesign. A pre-commitment for the next unit. Thirty minutes. The most important thirty minutes of any unit — the part most schools skip.

Forward Lens Worksheet 05 Impact

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What’s next

This is the last of the five pillar deep-dives. Next post: the synthesis — Bringing the Forward Lens to your school: the year-one implementation roadmap. The first quarter, the second quarter, the third, and the fourth. What to do, who to involve, and what to measure. If you’ve read every post in the series, the framework is now in your hands. The roadmap is how you use it.

That uncertainty arrived almost overnight. For most of the history of schooling, the line between a student’s work and someone else’s was reasonably easy to see; plagiarism left fingerprints. Generative AI erased the fingerprints. A tool that can produce clean, original-sounding prose on any topic in seconds doesn’t just enable cheating — it dissolves the very thing assessment depends on: the assumption that the work in front of you reflects the mind that turned it in. That assumption is gone, and no detector is bringing it back.

Intention asks why this tool, here, now? Investigation asks what information skills are in play? Implementation asks how are students engaging? The fourth pillar — Integrity — asks the question generative AI made unavoidable: How do we verify and attribute? And by this I am not saying that AI use by students = cheating. That’s actually antithetical to what I have heard and seen from students. Additionally, most students, including any time AI was mentioned at a commencement address this year, were just not into hearing about it. So by no means is this pillar conflating AI with student cheating, but rather helping students navigate the constant stream of SLOP and misinformation.

Integrity is the pillar that did not exist in any previous technology integration framework, because the previous frameworks predated generative AI. SAMR (2010), TPACK (2006), even UDL 2.2 — none of them have language for the central question of every assignment in 2026: whose thinking is this?

That gap is not theoretical. The September 2025 RAND survey of nationally representative samples of teachers, students, and school leaders found that over 80% of students reported their teachers had not explicitly taught them how to use AI for schoolwork — while 54% of students said they were using AI for school anyway. The same survey found that 45% of principals reported having any AI policy at all. The disclosure conversation is happening in the absence of any disclosure language.

This is what happens when the pace of the technology outruns the pace of the pedagogy. Pretending it isn’t happening — or treating AI integrity as only an academic-honor-code issue — leaves teachers without the language they need to teach the actual skill. And the actual skill is not “don’t cheat.” It is knowing what is yours, knowing what is the tool’s, and being able to show the difference.

The most useful research on AI integrity in K-12 is not the research about catching cheaters. It is the research about disclosure-as-pedagogy — teaching students to make their AI use visible, in the same way good academic work has always made source use visible.

The emerging best practice across these frameworks is what some researchers are now calling a three-tier disclosure scaffold: assignments are categorized as No AI (the student’s thinking must be unaided), Limited AI with disclosure (specific narrow uses are permitted and must be named), or AI-collaborative with citation (the tool is a working partner and its contributions are explicitly cited). The AI Disclosure (AID) Framework developed by Kari Weaver applies a similar logic at the higher-education level, adapting the Contributor Roles Taxonomy (CRediT) used in academic publishing. The principle is the same: transparency about contributions, baked into the assignment, not bolted on after.

At the union level, the AFT’s National Academy for AI Instruction — launched in 2025 with founding support from OpenAI — is training 400,000 K-12 teachers over five years on AI use that meets the Commonsense Guardrails standard. Whatever one’s view of the funding source, the scale of the training effort is the largest formal disclosure-norm-building initiative in U.S. education.

The most direct UDL 3.0 mapping for Integrity is the combination of Action & Expression and Engagement — and specifically UDL 3.0’s shift from “expert learners” to learner agency.

Agency is impossible without identity. Knowing what is yours — and what came from somewhere else — is not a compliance question. It is an identity question. UDL 3.0’s emphasis on cultivating learners who are purposeful and reflective, resourceful and authentic, strategic and action-oriented requires that students know which parts of their work are theirs in the first place. Disclosure norms make that identity question visible. They are not about catching cheaters. They are about helping students know themselves as learners in an era when a tool can do the visible work for them.

The team practice this week

A three-tier disclosure scaffold drafted for your specific department or grade level. A list of assignment types mapped to disclosure tiers. A teacher-facing one-pager and a student-facing one-pager. A check-in date. Ninety minutes. One department-wide AI use agreement your students will see consistently across every classroom for the rest of the year.

Forward Lens Worksheet 04 Integrity

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What’s next

Next post: the final pillar — Impact — and the question schools have spent four years avoiding. What did the post-pandemic 1:1 program cost students in attention, focus, and well-being? And what is our responsibility, as the adults in the building, to make that cost visible to them — and to ourselves? If Integrity is the pillar that asks whether we can tell the student’s work from the tool’s, Impact is the pillar that asks whether the screen is worth what it took to put it there.

The room had every visible marker of a high-functioning 1:1 program. It was also forty-three minutes of pure consumption — a video that could have played on one screen at the front of the room. And it is everywhere, because consumption is the path of least resistance. A device makes it frictionless to put information in front of students; it takes real design to get students to do something with it. Most schools bought the devices and never budgeted for the design. The result is classrooms that look busy and produce nothing.

Intention asks why this tool, here, now? Investigation asks what information skills are in play? The third pillar — Implementation — asks: How are students engaging? Are they creating, collaborating, problem-solving — or has the tool quietly become the assignment?

In the launch post, I argued that SAMR promised a ladder, and that after fifteen years of SAMR-driven PD, most documented classroom technology use never moved past the lowest rungs. Crompton and Burke’s review of fifty-seven studies supports this directly. Implementation is where “stuck at S” becomes most visible. Students complete digital worksheets that could have been paper. Students watch videos they could have watched as a class. Students play Kahoot quizzes that test recall but produce nothing. Students “use AI to brainstorm” by accepting the first three ideas the model offers. None of this is inherently bad. All of it is consumption. And when a 1:1 program is mostly consumption with a screen instead of consumption without one, the screen isn’t earning its place.

Implementation reframes the question. Instead of asking where on the SAMR ladder is this lesson? — a question that depends on a ladder most teachers were never given time or coaching to climb — Implementation asks: What are students producing in this lesson that they could not have produced without the technology, and would not have produced as well without it?

The case for active over passive is one of the most settled findings in education research. Seymour Papert’s Mindstorms (1980) made the original case for constructionism: knowledge is constructed most durably when students make something external they can share, critique, and revise. John Hattie’s Visible Learning meta-analyses show that the effect size of educational technology varies almost entirely with whether the use is student-centered or teacher-centered. Same tool, same school, same students — wildly different learning gains depending on what students were doing.

The ISTE Standards for Students, revised in 2024, make this explicit. The standards include Empowered Learner, Knowledge Constructor, Innovative Designer, and Creative Communicator — all four describe a student who is making, building, or producing. Not one of the seven ISTE standards describes a student who is consuming.

A few systems have made student creation the explicit organizing principle of their tech integration.

The principle of Action & Expression asks how students demonstrate what they know and act on their learning. UDL 3.0 emphasizes honoring “diverse forms of communication and expression, especially those historically marginalized.” That’s the equity dimension of Implementation. The question is not just whether students are active or passive. It is whose form of expression is the tool enabling, and whose is it foreclosing? A tech-integrated classroom that allows only one mode — say, typed text in a Google Doc — is foreclosing as much as it is enabling.

Three practices that surface the passive-to-active question in any classroom.

The team practice this week

A typical-week audit on a six-column continuum. A creation-ratio reading. Three concrete shifts the team will pilot in the next two weeks. A re-map date. Sixty minutes. One honest look at what students are actually doing on their screens.

Forward Lens Worksheet 03 Implementation

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What’s next

Next post: Integrity — the pillar that did not exist in any previous framework, because the previous frameworks predated generative AI. The disclosure conversation, the attribution conversation, and the question every assignment now has to answer: where does the student end and the tool begin? If Implementation is the test of whether students are making something, Integrity is the test of whether we can tell.

That gap is not a failure of intelligence or effort. It is the predictable result of a school system that handed every student a device and an internet connection and then assumed that finding information and judging it were the same skill. They are not. We taught a generation to retrieve, and somewhere along the way we stopped teaching them to verify. Now AI can manufacture a convincing source in seconds, and the bill for that gap has come due.

The Forward Lens is built around the questions schools skipped. The first pillar, Intention, asks why this tool, here, now? The second pillar — Investigation — asks what happens the moment a student starts pulling information through that tool: What information skills are in play once the tool is in students’ hands?

For all the time the EdTech field has spent talking about classroom technology, almost none of it has been spent on what students do with the information they encounter through it. SAMR talks about Substitution and Redefinition; it doesn’t talk about source evaluation. ISTE talks about Empowered Learner; it doesn’t, at the lesson-plan level, talk about lateral reading.

Most schools that have addressed information literacy at all have done so through the media literacy unit — a one- or two-week stretch, usually in middle or high school ELA. That was reasonable in 2008. After what students learned about consuming information during the pandemic, and with generative AI now in the mix, it isn’t enough. Source evaluation is not a unit. It is a habit. Habits are built lesson by lesson, in every content area, not once a year in English class.

Stanford researchers gave 3,446 high school students across twelve states a set of source-evaluation tasks. The finding was what they called “alarming consistency.” Across grade levels, students judged credibility by visual polish — the cleanness of a site, the smoothness of a video, the professionalism of a graphic — not by provenance. That study was eight years ago. Generative AI has now optimized for polish at industrial scale.

The most important research shift in the last decade is the move from “checklist” approaches — check the URL, check the About page, check the date — to what Sam Wineburg and Sarah McGrew at Stanford named lateral reading.

Lateral reading is what professional fact-checkers do. They don’t read the source in front of them top to bottom. They open new tabs. They search for what other sources say about the one they’re looking at. The evaluation happens outside the source, not inside it. In Wineburg and McGrew’s comparison study, fact-checkers were dramatically faster and more accurate than history PhDs and Stanford undergraduates at the same task. That finding is now the foundation of the free Stanford Civic Online Reasoning curriculum — thirty lessons, used in a large Midwestern district experiment that showed significant student improvement.

Generative AI doesn’t change the underlying skill. It raises the stakes. The UNESCO AI Competency Framework for Students, released September 2024, names explicit competencies for evaluating AI-generated content — twelve competencies across four dimensions, with a full “Ethics of AI” dimension.

The clearest example is Finland, which has woven media literacy into its national curriculum since the 1990s and formalized it through a National Media Education Policy, published in 2013 and updated in 2019. Finnish media literacy isn’t a unit. It runs across subjects from age three through upper secondary. As of January 2026, Finnish teachers are explicitly adding AI literacy — including teaching fourth graders to identify AI-generated images. Finland consistently ranks first in Europe in the European Media Literacy Index.

In the U.S., the News Literacy Project’s Checkology platform reaches hundreds of thousands of students. The Stanford Digital Inquiry Group’s COR curriculum plays the same role for any teacher who can spare thirty minutes.

At the policy level, the European Commission’s Digital Education Action Plan includes an explicit media literacy strand, and the OECD’s PISA framework now includes the ability to distinguish fact from opinion as a measured competency. The international direction is consistent: information evaluation is a core subject, not an add-on.

The most direct mapping is Representation — the principle that asks how information is made available, perceived, and made meaningful. UDL 3.0 emphasizes “authentically representing a diversity of identities, perspectives, and narratives.” Investigation puts that emphasis into the moment a student encounters a source. Whose perspective is this? Who is missing? What identities are foregrounded — and which are flattened or erased? These are questions about the full information ecology students operate in, including AI tools trained on data that systematically under-represents some perspectives.

The team practice this week

A cross-curricular source audit. One shared lateral-reading routine designed to drop into any subject. Unit-level commitments from each team member. A pre/post evidence plan. Sixty minutes. One shared routine your students see in every classroom for the rest of the unit.

Forward Lens Worksheet 02 Investigation

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What’s next

Next: Implementation — the passive-to-active continuum, the difference between students using a tool and students creating with it, and the international evidence that consumption-mode tech use is the single most common failure mode of 1:1 classrooms.

If Intention is the question that opens the lesson, and Investigation is the practice that shapes what students do with what they find, Implementation is the test of whether students are making something with what they have learned — or just consuming more of it.

Then March 2020 happened.

Overnight, 1:1 stopped being a strategy and became an emergency. Devices went home — to every student, every grade, every subject, all at once. Daily screen time exploded without limit. Kids lived on screens. So did we. Endless scrolling became the default mode of how a generation took in information, stayed connected, and waited out a world that had stopped working. There was no time to ask whether any of it was pedagogically sound. There wasn’t supposed to be.

When schools came back, the buildings reopened but the screens never quite left. Schedules normalized. Devices didn’t. The 1:1 program that had existed as an emergency response stayed in place as if it had always been the plan. In most schools, it had not.

Many schools are still operating in that pandemic posture. Screens are a daily access point for every student, all day, without anyone ever pausing to ask why. The one thing I’ll grant the anti-EdTech movement that has emerged in the last two years: it has forced schools to start asking the question. To recalibrate. To pause.

The Forward Lens is built for that recalibration. And the first pillar — Intention — is where it begins.

Intention asks one question, before the device is plugged in: Why this tool, here, now?

SAMR — Substitution, Augmentation, Modification, Redefinition — has been the dominant framework for classroom technology integration for fifteen years. It gave us a vocabulary. It also skipped the question that comes before the four rungs: should this lesson involve technology at all?

Hamilton, Rosenberg, and Akcaoglu (2016) called this SAMR’s “absence of context.” The model assumes the teacher has already decided technology belongs in the lesson. It only helps her think about the level of integration she’s reaching. That assumption was tolerable in 2010. In a post-pandemic 1:1 environment, where the device is just there by default, it’s a recipe for screen-first design. Crompton and Burke (2020) reviewed fifty-seven studies and found that most classroom practice never moved past the lowest levels of SAMR anyway. Substitution dressed up as transformation.

Purpose-first design is not new. It is foundational instructional theory. Backward Design (Wiggins & McTighe) puts technology decisions at the end of lesson planning — after the goal and the evidence of mastery are defined. TPACK (Mishra & Koehler) places pedagogy and content as primary, with technology serving them. Hattie’s Visible Learning meta-analyses show that the effect size of educational technology varies almost entirely with whether the use is anchored to a clear pedagogical purpose. The tool is not the variable. The fit between tool and purpose is.

None of these are perfect. None have solved the post-pandemic default-on problem. But they share a posture: the question of purpose precedes the question of tool, at the level of policy, not just rhetoric.

The Forward Lens sits on top of UDL 3.0 (CAST, July 2024). For Intention, the key connection is goal clarity. UDL 3.0’s biggest shift — from cultivating “expert learners” to cultivating learner agency — depends on goals being transparent and meaningful to learners. When a teacher cannot articulate why a tool is in her classroom, neither can her students. When neither can articulate it, the tool becomes the thing, and the learning becomes whatever happens to occur in its presence.

Three practices. None require new tools or new initiatives.

• Before any tech-integrated lesson, three questions:

  • What is the learning goal?

  • Is technology actually the right medium?

  • If this could be done as well or better without a screen, why am I using one?

• For new tool adoption, one written sentence: Every new platform, app, or AI tool gets a one-sentence purpose statement attached — naming the learning outcome it serves.

• For team practice, one quarterly audit: Every department or grade-level team, once a quarter, sits down with the full list of tools in use and asks whether each is still earning its place.

The companion worksheet is built to run the audit your team has been avoiding. A three-lesson inventory. A team tool census. Five honest discussion questions. Three concrete pruning decisions with named owners and dates.

Forward Lens Worksheet 01 Intention

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What’s next

Next post: Investigation — what information literacy looks like at the lesson level in an AI-saturated classroom. Stanford’s Civic Online Reasoning data, UNESCO’s AI Competency Framework, and what Finland has been doing since the 1990s.

If Intention is the question that opens the lesson, Investigation is the practice that shapes what students do with what they find.

I’ve been reading your work on recalibrating educational technology. When you write about teacher pedagogy, what instructional technology pedagogy model do you actually recommend? The EdTech leaders in districts around me are still talking about SAMR. I’m thinking about UDL. I feel like we need a refresh on tech integration pedagogy models — if that even exists.

What this reader is naming is real in classrooms across the country. This is the part that doesn’t typically get the headlines, but is used to generate them. And the question is not isolated — I’ve been hearing some version of it from teachers, coaches, and instructional leaders for at least a year now.

Walk into almost any EdTech leadership conversation in 2026 and you’ll hear the same four letters: SAMR. It is the framework an entire generation of district leaders, instructional coaches, and tech integrators built their professional identities around. It is on PD slides. It is in observation rubrics. It is the shared vocabulary of a discipline.

SAMR was published in 2010 — before generative AI, before the attention crisis, before we understood what information literacy required in a post-search era, before any teacher had to ask a student whether a paragraph was theirs or the model’s. It was a meaningful contribution at the time. It is not, in 2026, the lens a classroom teacher needs on Tuesday morning.

The reader’s question — if that even exists — points at a real gap. The pedagogical conversation around classroom technology has not kept pace with the technology itself. And the vacuum is being filled by vendor pitches and headline panic instead of by serious pedagogy.

This post is the start of another series that I have been working on to focus on the classroom lens. Last week I wrapped up the Intentional Technology & Screen Framework. A framework designed for the school leader who is getting outside pressure to define why they have a 1:1 program and how much time during a day does a student spend on a screen, and is that screen time effective.

Before I introduce the framework, the honest thing is to name what’s already on the table. SAMR (Substitution, Augmentation, Modification, Redefinition) gave us a vocabulary for thinking about whether technology was transforming learning or just substituting for older tools. That was a real contribution.

But here’s the thing nobody wants to say out loud: after fifteen years of SAMR-driven PD, most classroom technology use has never moved past the S. Walk into ten classrooms tomorrow and you will see digital worksheets, Google Slides instead of poster board, Kahoot quizzes instead of paper exit tickets, YouTube videos played to thirty kids on thirty screens. Substitution. Sometimes augmentation. Rarely modification. Almost never redefinition.

This is not a failure of teachers. Teachers are doing what they can with the time, training, and tools they have. It is a failure of the model itself. SAMR promised a ladder, but the rungs above Substitution turned out to be invisible without sustained coaching, planning time, and pedagogical scaffolding that almost no district has ever fully provided. A framework whose own ladder doesn’t get climbed in practice is not a framework that’s working. It’s a vocabulary that’s working — which is a very different thing.

And even where teachers have climbed the ladder, SAMR has no language for the things that now define classroom technology: source verification, AI disclosure, attentional cost, student agency. Those omissions were tolerable in 2010. They are disqualifying in 2026.

UDL — Universal Design for Learning, recently updated to UDL 3.0 by CAST in July 2024 — is something else entirely. UDL is the equity foundation any tech-integration framework should rest on. UDL 3.0 in particular reframes the goal of learning around learner agency, identity, and the systemic barriers that shape how students experience school. It is essential. It is also, by design, not a tech-integration framework. UDL tells teachers how to design for variability. It does not tell them when, why, or to what end technology enters that design.

TPACK (Technological Pedagogical Content Knowledge) describes what teachers need to know to integrate technology well. It is a map. It is not the question a teacher asks during planning or a coach asks during a debrief.

What we don’t have is the everyday lens. A practitioner-scale framework. A small set of questions a teacher can ask before a tech-integrated lesson, during it, and after — that hold up in the AI era and that don’t require climbing an invisible ladder to be useful. Today I’m publishing that framework. It’s called The Forward Lens.

Where the Forward Lens sits

The Forward Lens is the classroom-level companion to the Intentional Technology & Screen Framework. If you’ve followed my work for a while, you know ITSF is the framework for boards and superintendents — the strategy-and-culture-level recalibration tool. ITSF asks: what kind of school do we want technology to serve? The Forward Lens asks a different question, one altitude lower: how should we teach with technology, day to day? Same values. Same research base. Different audience. ITSF was the program-level work. The Forward Lens is the classroom-level practice that makes the program real.

The Forward Lens V1

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It is built for three roles, with one set of questions:

• Classroom teachers — as a lesson-design checklist for planning.

• EdTech coaches and instructional integrators — as an observation and conferencing rubric.

• Department and grade-level leaders — as a unit-review protocol.

The Forward Lens does not replace UDL — it sits on top of it. UDL 3.0’s three principles — Engagement, Representation, and Action & Expression — define how a classroom is built for variability and learner agency. The Forward Lens adds five questions that are specific to technology integration and that UDL was never intended to answer. UDL gives us the design substrate. The Forward Lens gives us the technology-specific questions that emerge when AI, screens, and platforms enter that design. If you’ve ever asked, “Is this UDL-compatible?“ — the answer is yes, by construction. The full mapping is in the framework PDF.

The Five I’s

Here are the five pillars. Each is a question. Each gets a deep-dive post of its own over the next few weeks..

01 · Intention — Why this tool, here, now?

The dominant pressure in 2026 is tool-first design. The EdTech industry releases an AI-enabled product almost every month, and teachers are asked to evaluate, adopt, and integrate at a pace no instructional design process can sustain. Intention is the discipline of refusing that pressure — anchoring every use of technology to a learning goal before the tool enters the room.

02 · Investigation — What information skills are in play?

Generative AI has collapsed the cost of producing plausible-sounding falsehoods. Stanford research continues to show that without explicit instruction, students — even high-performing ones — evaluate sources by visual polish, not provenance. AI optimizes for polish. Investigation makes source evaluation and AI verification a lesson-level practice, not a once-a-year media literacy unit.

03 · Implementation — How are students engaging?

The default mode of most consumer technology is passive. Bringing that posture into the classroom unchanged is the single most common failure mode of classroom tech use. Implementation asks whether the technology has become an end in itself — or whether it is still serving learning. The tool is not the assignment. The learning is.

04 · Integrity — How do we verify and attribute?

Integrity is the pillar that did not exist in any previous framework, because the previous frameworks predated generative AI. The boundary between the student’s thinking and the tool’s contribution is now the central pedagogical question of every assignment. Pretending it is not — or treating it only as an academic-integrity policy problem — leaves teachers without the language they need to teach the actual skill.

05 · Impact — What did this cost or add?

Gloria Mark’s research documents the collapse of sustained focus on screens to roughly 47 seconds. Bringing classroom technology in without addressing attentional cost is bringing in the cost without the corresponding pedagogical benefit. Impact is the question that makes that trade visible — to the teacher, and eventually to the student.

Over the next few weeks, I’ll do a deep dive on each pillar. Each post will dig into the research, the UDL 3.0 connection, the AI-era stakes, and — most importantly — what this looks like on Tuesday morning in an actual classroom. If your school or district wants to bring this work in as a half-day workshop, a faculty PD series, or as part of a larger EdTech audit, that’s what I do — reach out.