TERITechnology
Ecosystem Readiness
Index for K-12

Board-adopted multi-year strategic vision for the K-12 technology ecosystem — names policy, practice, and student development (cognitive development, attention, ethical reasoning) as connected concerns, not separate initiatives. Distinct from 1.3 AI Policy (formal governance) and 1.4 AI Use Guidelines (operational); this is the binding multi-year strategic frame.

Why it matters A shared vision lets decisions cohere across tools and across time. The vision worth auditing places human learning first, treats technology as a force to be intentionally shaped, not merely adopted, and names cognitive and ethical foundations as first-class concerns alongside academic achievement.

Example resource TERI Self-Audit (prompt-ed.org/teri/assessment) + ISTE Essential Conditions for Effective Technology Use

Structured, ongoing learning for trustees / governing board members and senior district leaders (superintendent, cabinet, principals) on the K-12 technology ecosystem — so governance and oversight rest on shared substantive understanding, not staff briefings at the moment of decision.

Why it matters Strong governance on AI and technology starts with shared learning across both boards and senior leadership. Structured ongoing education — peer briefings, researcher conversations, school visits — equips trustees and district leaders to engage substantive questions, support thoughtful innovation, and bring meaningful judgment to decisions that increasingly come up at every meeting.

Example resource NSBA AI and EdTech guidance for boards; AASA leadership resources for superintendents

Board-adopted formal governance: scope, definitions, permissions and prohibitions, role differentiation, PII boundaries, vendor DPA requirement, attribution expectations, academic integrity definition, consequences and due process, safety protocols, review cadence, and authority for administrators to issue Guidelines (1.4).

Why it matters District-level AI policy gives teachers, students, and families consistent ground to stand on. It also gives the district legal standing for vendor agreements, discipline decisions, and FERPA responses. Without it, AI use varies classroom-to-classroom even when teachers are doing their best in real time.

Example resource EDSAFE AI Alliance SAFE Benchmarks

Administratively-issued operational guidance translating Policy (1.3) into daily practice — role-specific use cases, grade-band differentiation, prompt examples, tool-by-tool notes, attribution templates, AI-transparent assignment design, suspected-misuse procedures, and family-facing materials.

Why it matters Policy (1.3) sets what is required; Guidelines describe what good practice looks like in the classroom tomorrow. Guidelines give staff a shared playbook and absorb the pace problem — AI iterates faster than board meetings can keep up, and Guidelines can be revised without re-opening Policy each time.

Example resource Prompt-Ed AI Student Use Guidelines (prompt-ed.org/guidelines) + TeachAI Toolkit

After student data is collected — formal policies and operational practices for collection, storage, sharing, retention, and protection. Vendor DPA enforcement, central data inventory, staff annual training, breach response, FERPA/COPPA compliance baseline.

Why it matters Student data deserves protection beyond the FERPA / COPPA floor. Real-world risks — vendor data sharing, identity theft, algorithmic profiling, AI model training on student work — are why districts invest in data governance.

Example resource SDPC National Data Privacy Agreement (NDPA) + CoSN Trusted Learning Environment (TLE) Seal Program

Before any EdTech tool is adopted — formal pre-adoption review for instructional value, privacy, security, accessibility, equity, and total cost. Applies to paid, free, and teacher-initiated tools. Annual shadow-IT sweep surfaces unauthorized tools already in use.

Why it matters Tools adopted informally — teacher sign-ups, freemium apps, browser extensions — often arrive without the privacy review, training, or use guidance that protect students and staff. A vetting process is the upstream step that gives data governance, PD, and use guidelines something to work on; teachers benefit too, because they know which tools are cleared.

Example resource CoSN K-12 Community Vendor Assessment Toolkit (K-12CVAT) and the Student Data Privacy Consortium National Data Privacy Agreement (NDPA)

Stated district approach to online access and filtering — from maximum restriction to teaching student judgment. Articulates how filter decisions connect to instructional and developmental goals rather than defaulting to vendor settings, with override-request and review processes.

Why it matters CIPA is the legal floor. Beyond compliance, filtering decisions reflect district values — about student judgment, research access, and safety. The opportunity is to make filtering a deliberate pedagogical choice that staff, families, and students can understand.

Example resource AASL intellectual freedom and filtering resources

Annual audit of device access, connectivity, home-learning support, assistive technology, AI-tool access and use, digital-citizenship instruction, AI-literacy curriculum access, and academic integrity cases — disaggregated by subgroup, with identified gaps addressed in budget cycle.

Why it matters The digital divide is real, and AI is widening it. Students with less home support face more academic-integrity scrutiny, see less benefit from AI tools, and face greater exposure to online risk. Equity isn't a Layer 5 afterthought — it cuts across every checkpoint, and this audit makes that visible so districts can act on it.

Example resource Kapor Foundation — Responsible AI and Tech Justice

Underlying technical operations every other checkpoint depends on — network, device lifecycle, help desk staffing, cybersecurity, IT personnel capacity. A light-touch TERI checkpoint that points to specialized frameworks (CETL, CISA, NIST CSF 2.0, MS-ISAC) for depth.

Why it matters Strong policy, curriculum, and culture all depend on infrastructure that quietly works — wifi that holds, devices that boot, help that arrives when something fails. TERI names this dependency and points to specialized IT instruments (CETL, CISA) for the depth this layer deserves.

Example resource CETL framework (cetl.cosn.org)

Governance over AI in administrative decisions — behavior monitoring (Gaggle, Bark, iboss, GoGuardian), early-warning attendance and dropout systems, hiring resume-screeners, scheduling AI, special-ed AI, discipline-disparity flagging. Distinct from classroom AI (1.3 / 1.4): the algorithms making decisions about students behind the scenes.

Why it matters AI is increasingly used for high-stakes administrative decisions — flagging students for safety intervention, predicting dropout risk, screening teacher applicants, allocating resources. These systems are often built on biased training data. A district can have rigorous student-facing AI governance and still benefit from auditing the algorithms making consequential decisions about students behind the scenes.

Example resource EDSAFE SAFE Benchmarks — administrative AI lens

K-12 articulated scope and sequence for digital literacy — keyboarding, online safety, research and source evaluation, digital communication, digital citizenship — grade-band differentiated, subject-integrated across core content areas, with an articulated exit profile for graduating students.

Why it matters A written scope and sequence makes digital literacy a districtwide expectation rather than relying on individual teacher initiative. With it, students in the same district leave elementary school with shared foundations regardless of which classroom they sat in.

Example resource Common Sense Education Digital Literacy Curriculum

K-12 articulated scope and sequence for AI literacy — how AI works, when (and when not) to use it, output evaluation, algorithmic bias, ethics, cognitive implications — grade-band differentiated, subject-integrated, with an exit profile (anticipating HB 4005-style AI instruction mandates emerging across states).

Why it matters AI literacy is not digital literacy. Students using AI tools without understanding them form habits of uncritical acceptance, lose practice with the underlying skills the tools replace, and miss the cognitive work the assignment was designed to develop. Arizona HB 4005, currently pending in the state legislature, would require K-12 instruction on the ethical, moral, and educational uses of AI beginning in the 2027–28 school year. Comparable proposals are advancing in Hawaii (SB 2212), Florida (HB 1503/SB 1694), and New Jersey (A.4352/S.2862), while New York's Responsible AI Safety and Education Act (signed December 2025) takes a broader regulatory approach. AI literacy needs its own coherent K-12 treatment.

Example resource AI4K12 — Five Big Ideas (ai4k12.org)

K-12 developmental progression for cultivating student agency over their personal tech use: K-2 protect attention; 3-5 name algorithmic dynamics; 6-8 confront persuasive design; 9-12 audit your own use. Curriculum is one tool inside the strategy; the outcome is graduates who can navigate the attention economy.

Why it matters Students benefit from explicit, sustained development of agency over their personal tech use across grade bands. A single screen-time rule or one digital-citizenship unit doesn't build the cognitive muscle needed to navigate persuasive design, algorithmic dynamics, and attention economics. A K-12 progression does.

Example resource Common Sense Digital Literacy & Well-Being Curriculum

How the district defines, communicates, and operationalizes academic integrity in an AI era — board-adopted policy (definitions of authorized vs unauthorized AI use, attribution, consequences, appeals) plus aligned classroom practice (AI-resilient assignment design, oral defenses, fair-process protocols, explicit recognition that AI-detection tools are unreliable evidence).

Why it matters Legacy academic integrity language was written before generative AI. "Plagiarism" and "cheating" no longer map cleanly onto a landscape where AI can draft, edit, or co-author student work. Updated policy alongside aligned classroom practice keeps enforcement clear and fair, especially for students still learning the new expectations.

Example resource International Center for Academic Integrity (academicintegrity.org)

Rethinking evaluation for the AI era — rubrics, authentic tasks, process-based assessment, standards-based grading — so what is measured is student learning, not AI output. Distinct from academic integrity (2.4): this is what counts as evidence of learning.

Why it matters Most assignments were designed assuming students worked alone with limited resources. AI changes that assumption. Assessment redesign keeps grades measuring learning rather than AI proficiency and protects equity for students with and without easy access to AI tools.

Example resource Understanding by Design (Wiggins & McTighe)

Developmentally appropriate Pre-K-2 approach — analog foundations, sustained attention, play, social development, executive function. Technology used purposefully, not by default. Aligned with current AAP digital media guidance (2026) and the NAEYC/Fred Rogers Center 2012 joint position statement on technology in early childhood.

Why it matters Early childhood is where cognitive foundations are built — sustained attention, early literacy, executive function — and those foundations determine whether older-grade curriculum lands. K-2 deserves a deliberate, developmentally-grounded approach rather than scaling down older-grade tech practice.

Example resource NAEYC position statement on technology in early childhood

K-12 articulated pathway for computer science and CTE tech competencies — programming, computational thinking, data science, cybersecurity, and field-specific tracks (IT, healthcare, trades, design, manufacturing). Distinct from 2.1 / 2.2: the builder layer beyond user fluency, with industry credentials and graduation pathways.

Why it matters Digital and AI literacy give students fluency as users; CS and CTE give them fluency as builders, problem-solvers, and credentialed practitioners. An articulated K-12 pathway makes CS available to every student rather than dependent on family advocacy.

Example resource k12cs.org — K-12 Computer Science Framework

Ongoing, context-specific, job-embedded PD covering technology, AI, media literacy, and digital wellness — new-hire onboarding, annual training, role / subject / experience-differentiated pathways, sustained over time rather than one-shot training events.

Why it matters One-shot workshops produce limited durable change. Structured, ongoing PD — including real onboarding for new hires — is what brings district policies and curriculum expectations into classroom practice. PD is where every upstream decision either lands or evaporates.

Example resource Learning Forward Standards for Professional Learning

Coaching and chronic-support layer — instructional coaches, tech integration specialists, peer mentors, protected co-planning time. Translates PD learning into durable classroom practice, addressing the implementation gap PD alone cannot close.

Why it matters PD lands when coaching closes the loop. One-time workshops can spark interest; sustained coaching is what carries new practice into classrooms. Districts that fund and structure coaching deliberately see the shifts they were aiming for.

Example resource Jim Knight — Instructional Coaching

Structured mechanisms for students to participate in district tech / AI / media policy and curriculum decisions — committee representation, regular consultation, and authority to influence what gets adopted.

Why it matters Students are the ones experiencing the technology environment being designed for them. Policy made with their input is more pedagogically grounded, more accurate about what's actually happening (phone use, AI use, platform use), and more durable. Student voice is the highest-leverage student-facing capacity in an otherwise adult-focused layer.

Example resource Mikva Challenge — student voice and youth advisory boards

Expectations for how teachers themselves use technology, AI, and media in their professional work — lesson prep, grading, parent communication, classroom modeling. Practice models the message; without it, student-facing AI literacy work loses credibility.

Why it matters Adults model the technology relationship as much as students do. The way teachers use phones, AI, and other tools in their own work shapes what students see modeled — which is why intentional teacher modeling reinforces the curriculum rather than running parallel to it.

Example resource TeachAI Toolkit — teacher modeling guidance

Expectations for how superintendents, central office, principals, and other leaders use technology and AI in their own work, and how they visibly model intentional practices. Leadership use is the cultural ceiling for everyone else.

Why it matters Administrators set culture through visible practice. When leadership models the same intentional use the district asks of teachers and students, espoused values become observable practice. Admin modeling is the upstream signal that authorizes downstream expectations — teachers notice, and so do students.

Example resource All4Ed Future Ready Schools (futureready.org)

Integration of certified librarians and media specialists into curriculum planning, teacher PD, student instruction, and tech/AI governance — treating them as core instructional staff, not resource-room logistics.

Why it matters Many librarians and media specialists have deeper formal training in information literacy, research skills, digital citizenship, and intellectual freedom than almost any other role in the school. Strong districts integrate them into Layer 2 and 3 work as core instructional partners, where their expertise is most needed.

Example resource AASL National School Library Standards

Integration of counselors, social workers, psychologists, and mental-health staff into the tech / AI / media wellness ecosystem — through training, protocols, curriculum partnership, and shared response to incidents.

Why it matters Media exposure, AI-mediated harassment, sextortion, cyberbullying, algorithmic amplification of harmful content, and recommender-driven exposure often surface first in the counselor's office, not the principal's. Bringing mental health staff in as upstream partners — not just downstream responders — catches earliest signals and creates the best prevention opportunities.

Example resource ASCA Mindsets and Behaviors for Student Success

Documented procedures for tech-mediated incidents — cyberbullying, AI-generated harassment, deepfakes, sextortion, AI misuse, data breaches. Defines roles, communication pathways, coordination with mental-health staff, law enforcement, and families. Distinct from 2.4 (academic integrity).

Why it matters Tech-mediated incidents are happening in every district. A documented protocol turns response into something districts can do consistently — supporting students, managing legal exposure, and capturing prevention insight. Deepfake harassment and sextortion in particular have escalated faster than most district protocols have kept up.

Example resource CISA K-12 cybersecurity and incident response resources

Board-adopted policy governing student personal devices — phones, smartwatches, earbuds, tablets, personal laptops, emerging wearables — defining where, when, and how they may be used and stored across the school day. Pairs with 4.2 Practice & Pedagogy.

Why it matters A clear personal-device policy gives teachers, students, and families shared ground rules. Without it, expectations vary teacher-by-teacher, which can read as unfairness. Many states now require districts to adopt these policies, so state-law alignment is increasingly the floor.

Example resource Common Sense Education — Cell Phone & Personal Device Policy resources

How the device policy lives day-to-day. Both the cultural layer (shared rationale, family understanding, teacher consistency on transitions and on-the-spot decisions) and the spatial layer (storage, charging, layout, signage, device-free zones, transition routines) shape the answer. Pairs with 4.1 binding policy upstream and 2.3 student-agency curriculum it scaffolds.

Why it matters Compliance & Structure (4.1) sets the rules; this checkpoint asks whether the rules shape daily behavior. Strong spatial setup (storage, charging, signage) and strong cultural conversation (shared rationale, family understanding) work together; either one alone leaves a gap.

Example resource Center for Humane Technology — Foundations of Humane Technology (humanetech.com)

District-level expectations for how much, when, and for what purposes students use screens — grade-band and subject differentiated, attending to quality (what's on the screen) and to the trade-off with non-screen learning time.

Why it matters Screen time isn't homogeneous — thirty minutes of deep reading is not the same as thirty minutes of TikTok. The most useful screen-time norms attend to what students are doing on the screen and what learning it serves, not just minutes.

Example resource American Academy of Pediatrics — screen time guidance

Intentional analog practices — handwriting, physical books, in-person discussion, sustained silent reading, art and making, physical movement, face-to-face collaboration, unstructured outdoor time — as cognitive counterweights to screen-mediated learning.

Why it matters The brain develops through what it practices, as cognitive science research has documented. A school day with deliberate analog and counterweight practices protects cognitive capacities (sustained attention, deep reading, face-to-face attunement) that can otherwise atrophy. Counterweights are intentional protection — they hold space for capacities that screen-mediated learning doesn't develop on its own.

Example resource Maryanne Wolf — Reader Come Home

Tech and AI dimensions of special-education service delivery — assistive devices and software, IEP-aligned accommodations during AI use, sped-IT-curriculum coordination, AI accommodation accuracy review, IDEA compliance. Distinct from 1.8 (general equity audit): the dedicated audit for students with IEPs and 504s.

Why it matters Federal law (IDEA) requires the IEP team to consider the assistive technology needs of every child with an IEP (34 CFR §300.324(a)(2)(v); 20 U.S.C. §1414(d)(3)(B)(v)), and to ensure AT devices and services are made available when required for FAPE (34 CFR §300.105). The depth of that consideration matters — it's most effective when AT is treated as ongoing curriculum partnership rather than one-time procurement. AI tools introduce new accommodations whose accuracy varies, making rigorous review more important.

Example resource QIAT — Quality Indicators for Assistive Technology

Families engaged as partners — family-facing communication, parent learning resources, plain-language policy summaries, home guidance on device and screen use, AI literacy for parents, structured input channels, crisis-communication protocols, and a public AI-tools / data-collection document.

Why it matters Students' technology lives span home and school. When families and schools share understanding of AI, algorithms, attention, and digital wellness, what schools teach is reinforced at home. Family partnership is also where district credibility on digital wellness work is built — and where families bring valuable signal about what's actually happening in students' lives.

Example resource Dr. Michael Rich — The Mediatrician's Guide (2025)

How the district engages the broader community — local media, civic organizations, faith communities, businesses, local government, higher education, and community technology partners. Public-facing communication, partnerships, advocacy, and regional / national participation.

Why it matters Schools don't operate in a vacuum. The broader community brings resources, wisdom, concerns, and stake in district decisions. Two-way community engagement also builds the political support that AI, phone policy, and digital wellness work tend to need.

Example resource NSPRA — district communications resources

How the district measures whether tech / AI / digital wellness work produces intended outcomes — cognitive (attention, deep reading, judgment), academic, social-emotional, equity, family-engagement metrics. What is measured, how often, how analyzed, how it informs decisions.

Why it matters Outcomes measurement is the largest open question in K-12 technology work. Districts invest substantial resources in technology, AI policy, and digital wellness initiatives, and meaningful measurement is how they learn whether the investments are paying off — and where to adjust. Outcomes measurement is also where equity becomes visible.

Example resource Carnegie Foundation — Learning to Improve

Discipline of treating the tech ecosystem as a living system — annual review, policy revision, ecosystem recalibration, leadership transition continuity, TERI re-audit cadence. Distinct from 5.3 (outcomes): this asks whether the system itself is being maintained and improved.

Why it matters Even strong districts drift over time — new tools arrive, state laws change, staff turn over, student needs shift. Scheduled re-audit cadence is what keeps the work alive across leadership transitions and keeps current practice from being mistaken for last year's plan.

Example resource Carnegie Foundation — Networked Improvement Communities

Structured peer learning with other districts and regional networks — regional ed service agencies, national superintendent and CTO networks, peer-district learning partnerships, cross-district shared practices. Ecosystem-level work rarely succeeds in isolation.

Why it matters Districts working alone face higher costs, slower learning, and more risk. Networks share practice, distribute the cost of experimentation, and keep work alive through leadership transitions. Peer learning is also how districts know whether 'Expanding' on a TERI checkpoint is genuinely strong practice — or just best-informed about their own work.

Example resource NSBA + Digital Promise — League of Innovative Schools