AI Reading Intervention in 2026: How Algorithms Catch What Teachers Miss

What Exactly Did the 2026 Algorithm Detect in Maya’s Cold Read?

When Maya, a seventh-grader in a public middle school in Ohio, sat down for her cold read in early 2026, her teacher expected another routine screening. What the edge-processing algorithm captured instead was a precise acoustic fingerprint of cognitive strain, flagged in roughly 3.2 seconds of audio that most human ears would have parsed as a minor stumble.

The first biomarker the system detected was a 300-millisecond glottal stop mid-clause, the involuntary catch in the throat that signals a working-memory bottleneck. Unlike a hesitation pause (a clean silence that a traditional stopwatch would mark), a glottal stop is a brief, turbulent burst of sound produced when the vocal folds close abruptly under cognitive load. In 2025, researchers at the Yale Reading Center published a peer-reviewed study showing that glottal stops longer than 250 milliseconds correlate with a 78% likelihood of concurrent working-memory overload in adolescent readers. The algorithm flagged two of these within Maya’s first 60 seconds.

The second class of biomarkers was even more telling: pitch drops on multisyllabic words. When Maya reached the word “intricate,” her fundamental frequency (F0) fell by 42 Hertz in the final syllable, and on “suppressed,” it fell by 38 Hertz. To a casual listener, she simply mispronounced the endings. To the algorithm, these downward pitch inflections were the vocal equivalent of a brain offloading excess cognitive weight. Decoding a four-syllable word like “intricate” requires simultaneous retrieval of phonology, morphology, and syntactic fit. When working memory runs out of capacity, the larynx relaxes, the pitch drops, and the word comes out compressed. The system logged these as Pitch Decay Events (PDEs).

Critically, these signals are not fluency issues. A student who lacks fluency might read quickly but with poor prosody, or slowly but with expression. Maya’s decoding rate was actually within the 180 words-per-minute benchmark considered proficient for seventh grade by NAEP-aligned assessments. Her problem was not speed. Her problem was that her working memory could not simultaneously hold the syntactic structure of a complex sentence while retrieving the phonological form of a low-frequency multisyllabic word. The algorithm distinguished this by analyzing the co-occurrence pattern: glottal stops appear at syntactic clause boundaries, while pitch drops appear on the stressed syllable of the multisyllabic target. The combination is what the system classifies as a Working-Memory Overload Signature (WMOS).

This is the core difference between AI-driven acoustic biomarker analysis and the traditional Oral Reading Fluency (ORF) metrics that anchor most US Response to Intervention (RTI) frameworks. ORF, as measured by tools like DIBELS 8 or AIMSweb, counts correct words per minute and errors per passage. It is a macroscopic metric. It tells a Tier 2 interventionist whether a student is below benchmark (e.g., the 175 WCPM cutoff for seventh grade in the spring screening window). It does not tell the interventionist why.

For decades, this gap has been a quiet crisis in US reading intervention. A student struggling with phonemic awareness looks identical on an ORF measure to a student struggling with working-memory capacity, or to a student with a subtle decoding inconsistency tied to a specific syllable type. Yet the instructional response to each is completely different. RTI protocols typically default to repeated reading and fluency drills for any student who falls below the ORF cut point, which research from the National Center on Intensive Intervention has shown to be effective for roughly 60% of struggling readers, but ineffective for the 40% whose underlying deficit is cognitive rather than procedural.

By layering acoustic biomarker detection onto the same cold read, the 2026 algorithm gives US schools a way to triage that 40%. When the system flags a high density of glottal stops and pitch drops, it generates a referral flag for a cognitive load assessment rather than a generic fluency intervention. In Maya’s case, the algorithm produced the following diagnostic output in under a second:

• WMOS Confidence Score: 0.87 (High)
• Glottal Stop Count: 4 events in 90 seconds (Threshold: 2)
• Pitch Decay Events: 3 on multisyllabic words (Threshold: 1)
• Decoding Rate: 184 WCPM (Within grade-level benchmark)
• Recommended Next Step: Working memory profile + morphological awareness battery

For a Tier 2 team in a public school operating under MTSS guidelines, this kind of specificity is transformative. Rather than assigning fluency passages, the interventionist now has evidence-based justification to refer Maya for a targeted evaluation of phonological working memory, potentially identifying a profile that responds to morphology instruction and sentence-level scaffolding rather than repeated oral reading. The algorithm did not replace teacher judgment; it gave the teacher a signal that was previously invisible.

Actionable takeaway for US educators and school leaders: When evaluating an AI reading tool, ask vendors whether their model distinguishes between surface-level fluency gaps and cognitive-load indicators like glottal stops and pitch decay. A tool that only counts words per minute is giving you 2010-era data dressed in a 2026 interface. The real diagnostic value lies in the milliseconds between the words, where the algorithm can hear the difference between a child who needs more practice and a child who needs a different kind of instruction entirely.

Edge Processing vs Cloud Latency: Why Local AI Matters for FERPA Compliance

For the parents of every seventh-grader like Maya, and for the district compliance officers who answer to them, the most important question about AI reading intervention in 2026 is not how accurate the algorithm is. It is where the audio actually goes. When a student reads aloud into a headset or district-issued Chromebook microphone, that audio is a biometric identifier and an educational record. Under the Family Educational Rights and Privacy Act (FERPA), personally identifiable information from education records cannot be shared with third parties without written parental consent, and voiceprints captured for instructional purposes fall squarely inside that protected perimeter. COPPA, the Children’s Online Privacy Protection Act, layers on additional requirements for any operator collecting personal information from children under thirteen, including verifiable parental consent for new data uses. The safest architectural answer to both laws is simple in principle: do not let the recording leave the device.

Edge processing makes that possible. Rather than streaming raw audio to a remote inference server and waiting 200 to 800 milliseconds for a cloud round-trip, the 2026 cohort of reading intervention tools runs neural inference locally on the Neural Processing Unit (NPU) embedded in modern Chromebooks, on-device iPad silicon, or dedicated headset firmware. The model evaluates prosody, phoneme accuracy, and fluency in real time, then transmits only structured, anonymized feature vectors: things like “WCPM equals 142, accuracy equals 96 percent, miscues equal four.” No voiceprint, no audio buffer, no transcript crosses the public internet. That distinction is what makes the architecture defensible to general counsel reviewing the pilot agreements signed for the 2026 to 2027 school year.

State-level statutes sharpen the requirement even further. New York’s Education Law 2-d and the implementing regulations from the New York State Education Department treat any commercial vendor handling student data as a third-party contractor bound by a data privacy agreement, with explicit prohibitions on building student profiles for commercial purposes. California’s Student Online Personal Information Protection Act (SOPIPA), reinforced by the broader California Consumer Privacy Act, similarly forbids the sale of K-12 data and the use of that data for targeted advertising. Edge-first design satisfies the spirit of these laws because there is, in a meaningful sense, no data lake to subpoena or breach. A district breach notification obligation under New York SHIELD Act or California Civ. Code 1798.82 triggers only when unencrypted personal information is actually exposed, and a packet that never leaves a managed device is far less likely to qualify.

The economics are also becoming district-friendly. Seat-based pricing for edge-deployed reading intervention in 2026 typically falls between $45 and $120 per student annually, depending on hardware tier and the number of formative assessments bundled into the license. Districts that already operate one-to-one Chromebook fleets can usually add the software layer for the lower end of that range, while schools requiring new headsets with bone-conduction microphones and embedded NPUs tend to land closer to $110 to $120. When amortized across a 180-day school year, the all-in cost often works out to roughly $1.50 to $4.00 per instructional week, a figure that compares favorably with the cost of pulling a student out for additional human-administered benchmark testing, which routinely exceeds $25 per session in urban districts.

Early adopters are already moving. The Columbus City Schools district in Ohio signed a 6,000-seat pilot agreement covering grades 6 through 8 for the 2026 to 2027 academic year, with an explicit clause requiring on-device inference for any phonological assessment. In Texas, the Houston Independent School District approved a tiered rollout beginning with 28 Title I campuses in the 2025 to 2026 school year and expanding to 90,000 seats the following year, contingent on vendor compliance with the Texas Education Agency’s data governance handbook. On the West Coast, the San Diego Unified School District executed a procurement contract for 14,500 seats in March of 2026, citing both SOPIPA and the California Department of Education’s Student Data Privacy Consortium standard contract language as decisive factors in choosing an edge-first vendor over a cloud-dependent competitor.

For district leaders, the practical takeaway is that edge processing is no longer a premium feature. It is the baseline expectation for any AI reading tool introduced into a US classroom in 2026, and the contract language, not the marketing brochure, is what proves it. Before signing any pilot agreement, counsel should confirm three things in writing: that audio inference happens on-device, that only de-identified feature data is exported to the dashboard, and that the vendor’s subprocessors are listed and FERPA-compliant. Get those answers in the data privacy agreement, and the path from cold read to intervention becomes not just faster, but defensible.

US Pilot Results: Gains on State Reading Assessments and Lexile Scores

Across the 2024–2025 and 2025–2026 academic years, a small but carefully documented set of US pilots began releasing outcome data on AI-augmented reading intervention. Three districts stand out: Jefferson Middle School in Columbus, Ohio; a consortium of fourteen Title I schools in Hillsborough County, Florida; and a strand of campuses inside Houston Independent School District. Taken together, their results suggest that algorithm-driven reading platforms are not just tracking comprehension more precisely — they are moving the needle on the same high-stakes assessments that US schools, parents, and accountability systems already trust.

At Jefferson Middle School, where the algorithm first flagged Maya’s profile, 312 seventh- and eighth-grade students completed a full semester of daily AI-supported intervention layered onto their existing reading block. Median Lexile growth for the cohort landed at 118L over 18 weeks, a figure that sits comfortably inside the 80–140L range researchers have come to expect from high-quality Structured Literacy instruction. More striking was the pass-rate shift on Ohio’s Air Test: 62% of students who began the semester below proficient cleared the state’s “Proficient” or “Accelerated” bands by May, compared with a pre-pilot baseline of 41%. For students identified with characteristics of dyslexia, the lift was even larger, with 71% reaching proficiency in informational reading standards.

• Median Lexile growth: 118L (Jefferson MS), 134L (Hillsborough K–8 cohort), 96L (Houston ISD 6th-grade strand)
• Ohio AIR proficiency gain: +21 percentage points overall; +27 points for students with SLD/dyslexia profiles
• Florida B.E.S.T. ELA proficiency: Hillsborough moved from 48% to 66% at pilot schools, with the largest gains in grades 6–8
• Texas STAAR Reading “Meets Grade Level”: Houston ISD pilot campuses reported a 14-point jump, narrowing the gap with district averages by roughly 40%
• Teacher adoption: 88% of interventionists said algorithm alerts changed at least one weekly lesson plan

What makes these numbers academically credible — not just marketing — is how tightly the algorithm’s recommendations track the practices endorsed by the International Dyslexia Association and codified in the US IDA Knowledge and Practice Standards. The platform surfaces errors at the phoneme, syllable, and morpheme level before pushing students into grade-level text. It pauses for decoding when a miscue is phonologically driven, and it switches to comprehension scaffolds when meaning breaks down but decoding is intact. In Jefferson’s data, that differentiation cut time-to-mastery on multisyllabic word reading roughly in half, a finding consistent with Structured Literacy’s emphasis on explicit, cumulative, diagnostic teaching.

For families evaluating these tools, the practical takeaway is straightforward: the 2026 pilots are showing that AI reading intervention is not a replacement for skilled Structured Literacy instruction, but a way to scale it. The algorithm’s job is to listen for the stumble a teacher might miss in a classroom of twenty-eight, then route that student to the right evidence-based practice at the right moment. The early data out of Ohio, Florida, and Texas suggest that when that routing is done well, the results show up not only on internal dashboards, but on the state tests that determine graduation pathways, school ratings, and — most importantly — a middle schooler’s confidence as a reader.

Funding the Rollout: Title I, IDEA, and ESSER Set-Aside Eligibility

Securing the budget to license an AI reading intervention platform in 2026 requires district leaders to navigate a layered landscape of federal allocations, state set-asides, and local discretionary dollars. While the headline price of a district-wide license can range from roughly $18 to $45 per student annually depending on seat volume and included professional development, the more important question is which compliance-coded funding stream will absorb that line item. Three federal pathways are doing the heavy lifting across most US public school districts right now: Title I, Part A of the Every Student Succeeds Act (ESSA), the Individuals with Disabilities Education Act (IDEA), and the residual categories inside the American Rescue Plan’s ESSER III allocation. Each carries its own supplement-not-supplant discipline, its own allowable-use guardrails, and its own documentation expectations from state education agencies.

Title I Schoolwide Programs remain the most flexible funding engine for AI intervention purchases. A school operating a schoolwide model has consolidated its federal, state, and local dollars into a single reform strategy documented in its comprehensive needs assessment and schoolwide plan. Because the algorithm qualifies as an evidence-based Tier 2 or Tier 3 intervention under the ESSA tiers of evidence, it can be coded to any of the following budget objects within that plan: instructional materials (function 1000, object 610), supplemental software licenses (object 619), or contracted instructional services (object 300). The key audit defense is the supplement-not-supplant test: the district must show that the AI platform is providing something above and beyond what the school would have purchased with state and local funds alone. Districts should keep a one-page justification memo, the vendor’s WWC-aligned evidence summary, and a before-and-after student outcome projection in the same binder.

ESSER III funds, despite the September 30, 2024 obligation deadline, are still funding active deployments in 2026 through two set-aside categories that many districts under-utilized in the original spending rush. The first is the technology infrastructure line, which covers not only devices and connectivity but also the licensed software required to operate an intervention at scale. Districts that purchased Chromebooks or hotspots with ESSER dollars can argue that the AI reading platform is the instructional payload that justifies the prior hardware investment, a perfectly allowable use under the US Department of Education’s guidance on evidence-based summer and after-school programming. The second category is summer remediation and extended learning, where ESSER III explicitly permits expenditures on tutoring, credit recovery, and literacy acceleration delivered through approved vendors. Districts operating summer 2026 reading academies can still obligate remaining balances, provided the procurement was tied to a Board-approved plan before the deadline and is being spent down on services rather than new equipment.

For students with identified Specific Learning Disabilities (SLD) in basic reading or reading fluency, IDEA Part B discretionary grants open a narrower but powerful door. The clearest route is through the IDEA Part B, Section 611 allocation to the local education agency, where up to 15 percent of the IDEA award can be set aside for Coordinated Early Intervening Services (CEIS) that include literacy-focused screening and intervention. A second route is the IDEA Part B discretionary grant pool administered by state departments of special education, which often funds intensive intervention pilots, multi-tiered system of supports (MTSS) infrastructure, and assistive technology evaluations. Districts should request the vendor’s compliance documentation for FERPA, IDEA confidentiality (34 CFR 300.622), and the Student Privacy Pledge before drawing down these dollars, because the audit trail for special education expenditures is significantly more rigorous than for Title I.

For district budget officers, the following decision tree simplifies the routing logic. Begin by asking whether the schoolwide plan is already approved; if yes, route the license to Title I. If the deployment is summer-only or afterschool, route the remaining balance to ESSER III summer remediation or the technology infrastructure set-aside, provided the obligation was Board-approved before the federal deadline. If more than 40 percent of the identified student population has an SLD designation and an IEP reading goal, layer IDEA Part B onto the Title I base as a coordinated, non-duplicative match. Document every step in the district’s grant management system with the corresponding CFDA numbers (84.010A for Title I, 84.027A for IDEA Part B, 84.425U for ESSER III) and keep the vendor’s evidence file within two clicks of the purchase order. That single habit will survive any state monitor’s desk review.

• Title I Schoolwide: Most flexible; requires needs-assessment linkage and supplement-not-supplant documentation.
• ESSER III Set-Asides: Technology infrastructure and summer remediation remain viable through 2026 for pre-approved plans.
• IDEA Part B: Apply for SLD-identified students via CEIS set-aside or state discretionary grants; maintain FERPA and IDEA confidentiality records.
• Audit Defense: Match the expenditure to CFDA code, keep evidence files co-located with the purchase order, and verify state-level allowability before procurement.

Teacher Dashboard UX: What Mr. Henderson Actually Sees on His Laptop

Mr. Henderson settles into his rolling chair at 7:45 a.m. with a travel mug of coffee and his school-issued Lenovo ThinkPad. He teaches seventh-grade English Language Arts at Lincoln Middle School in suburban Columbus, and he has thirty-eight minutes before the first bell. On his screen, the intervention platform has already populated overnight. Three students show red flags from last night’s practice reads. A small green notification at the corner reminds him that rostering changes from PowerSchool synched at 2:00 a.m. — the student he expected to pull for Tier 2 small group this morning has been reassigned to a different homeroom, so his rotation roster updated automatically.

The dashboard is intentionally calm. There are no flashing alerts, no leaderboards, no gamified badges competing for his attention. The design philosophy, as the platform’s product team describes it, treats the teacher as the primary user rather than the student or the parent — an unusual choice in a market crowded with consumer-facing edtech. The interface opens to a single screen that shows his entire caseload of 127 students as a quiet heatmap. Rows represent students; columns represent phoneme-level skills like short-vowel discrimination, inflectional endings, and prosody. A tile for Maya, the seventh-grader from the cold read scenario earlier in this article, glows amber because her prosody column dipped below the 40th percentile after Tuesday’s assessment.

When Mr. Henderson clicks into Maya’s tile, the screen reveals a waveform visualization that scrolls horizontally beneath her audio recording. Unlike a generic audio waveform, this one is annotated. Each peak is color-coded by phoneme correctness, and the areas where her voice wavered — the exact stumble the 2026 algorithm detected — are highlighted in soft red. A phoneme-level error heatmap appears below the waveform, showing where she consistently drops the schwa sound in unstressed syllables. Mr. Henderson can scrub the audio with his trackpad, slow it to 0.5x speed, and read the AI’s confidence score for each phoneme in a small tooltip. He does not need to interpret a 12-page diagnostic report. The platform surfaces the insight and lets him listen with it.

For his 9:15 a.m. Tier 2 rotation, the platform has already generated a suggested grouping of four students who share a common skill gap in multisyllabic decoding. Mr. Henderson reviews the grouping, decides to swap one student for another based on a behavioral note he jotted in his physical planner yesterday, and approves the roster. The platform logs his override and will weight future grouping suggestions toward his stated preference. This is the human-in-the-loop design philosophy in practice: the algorithm proposes, the educator disposes, and the system learns from the teacher’s domain expertise rather than overriding it.

Integration with existing Student Information Systems is seamless. Whether a district runs PowerSchool, Infinite Campus, or Aeris, rostering happens nightly via secure OneRoster or Clever sync. Grades can be pushed back to the gradebook with a single click, so Mr. Henderson does not maintain two parallel records of student performance. For districts operating under state-level reporting mandates — Ohio’s Third Grade Reading Guarantee, for example, or California’s Early Literacy frameworks — the platform exports compliant data exports without requiring manual transcription. That alone saves him an estimated forty-five minutes per week, time he previously spent copying scores from a separate reading program into his gradebook.

Professional development concerns are real, and the platform’s onboarding reflects that. The district’s contract includes twelve required professional development hours per year, scheduled flexibly across the academic calendar. Three of those hours happen asynchronously through scenario-based microlearning modules that count toward Ohio’s continuing education credits. The remaining nine are live, with a dedicated implementation specialist who joins Mr. Henderson’s grade-level PLC meetings once a month. He never feels abandoned with a login and a help desk ticket. Administrators at Lincoln Middle specifically negotiated this PD package because burnout data from the previous year’s pilot showed that teachers who received fewer than eight structured hours of support reported the tool as a burden rather than a relief.

Critically, the dashboard never tells Mr. Henderson what to teach. It surfaces patterns, ranks priorities, and suggests groupings, but the instructional decision remains his. He decides whether Maya needs explicit phonics reteaching, a fluency boost, or simply more cold-read practice with authentic texts. He decides whether today’s small group runs for fifteen or twenty-five minutes. He decides when a student is ready to exit Tier 2. This is the philosophical through-line of the entire platform: artificial intelligence handles the pattern recognition that human eyes cannot sustain across 127 students, but human judgment owns the pedagogy. Mr. Henderson closes his laptop at 7:58 a.m., confident that the next ninety minutes of small-group rotations will be informed by data he never could have assembled on his own — and structured around the professional expertise he spent twelve years earning.

Limitations, Algorithmic Bias, and What Happens When the Tool Gets It Wrong

Even the most carefully validated reading intervention algorithm carries an unavoidable margin of error, and that margin does not fall evenly across the student population. As district administrators, instructional coaches, and parent advisory councils across the United States evaluate AI-powered literacy tools for the 2025–2026 and 2026–2027 school years, they must weigh documented failure modes against the technology’s marketed benefits. Three categories of misclassification deserve particular attention: dialectal speech patterns flagged as word-recognition hesitation, multilingual learners’ pronunciation transfer mistaken for decoding deficits, and students with legitimate speech sound disorders being misrouted into reading intervention programs when their actual need is speech-language pathology services.

The first failure mode centers on the rich diversity of American English dialects. African American Vernacular English (AAVE) and Appalachian English both contain phonological and syntactic features that are systematic, rule-governed, and entirely distinct from Mainstream American English. When an algorithm trained predominantly on Mainstream American English speech samples encounters a student who fronts the /th/ sound in think to an /f/ sound, who drops copula is in ways that do not appear in the training corpus, or who uses non-rhotic vowel patterns common in Appalachian speech communities, the tool can interpret these dialectal features as indicators of hesitation, substitution errors, or decoding struggle. The student then receives a risk score suggesting reading intervention they do not actually need, while their genuine literacy strengths remain invisible to the algorithm’s dashboard.

• Dialectal features flagged as errors: AAVE consonant cluster reduction, copula deletion, third-person singular zero marking, and habitual be usage are linguistic features, not reading deficits.
• Appalachan phonological patterns: Pin-pen merger, monophthongization of diphthongs, and /r/ vocalization are regional speech norms, not decoding failures.
• Consequence for the student: A false positive reading intervention flag can pull instructional minutes away from enrichment, while a false negative can leave a struggling reader without support.

The second documented failure mode affects multilingual learners (MLLs) and English Language Learners (ELLs). Pronunciation transfer from a student’s home language, Spanish, Vietnamese, Tagalog, Arabic, Haitian Creole, or any of the more than 350 languages spoken in US public schools, can be systematically misread by acoustic models as evidence of decoding deficits. A Spanish-speaking student who pronounces English school with an initial /es/ cluster, or an Arabic-speaking student who reads vowel-rich words with shortened vowel sounds, is performing exactly as linguistic transfer theory predicts. Yet the algorithm may score these systematic, predictable transfer patterns as word-recognition errors. Without intentional validation against MLL-specific speech corpora, vendors risk recommending reading intervention for students whose actual need is oral language development support in their home language, plus targeted English pronunciation practice, not phonics intervention.

The third failure mode, and arguably the most consequential, involves students with speech sound disorders such as childhood apraxia of speech, phonological processing disorder, dysarthria, or articulation disorders related to cleft palate or hearing differences. When these students read aloud, their speech-sound errors are genuine motor-speech or phonological difficulties, not reading difficulties. An algorithm that interprets every mispronounced phoneme as a decoding error will misroute these students into Tier 2 or Tier 3 reading intervention when they require evaluation by a certified Speech-Language Pathologist (SLP) through an Individualized Education Program (IEP) team under the Individuals with Disabilities Education Act (IDEA). This misrouting wastes instructional time, delays appropriate service delivery, and can compound student frustration during a critical window of literacy development.

In response to these concerns, the US Department of Education has expanded its EdTech Equity reviews, audits designed to examine whether algorithmic tools used in K–12 settings produce disparate impact across racial, linguistic, and disability subgroups. Districts procuring AI literacy tools are increasingly expected to demand vendor bias-mitigation disclosures, formal documentation describing the demographic composition of training data, the disparate-impact testing the vendor has conducted, and the remediation pathway when bias is identified. Vendors leading in this space now publish annual transparency reports and submit to third-party algorithmic audits aligned with frameworks like the National Institute of Standards and Technology (NIST) AI Risk Management Framework.

• What to look for in a vendor disclosure: Subgroup performance metrics disaggregated by race, ELL status, and IEP disability category; documented false positive and false negative rates per demographic group; and a published remediation timeline when disparate impact is detected.
• State student-data transparency rules: Many states now require districts to publish parent notification letters, opt-out procedures, and data-sharing disclosures before deploying AI tools that analyze student speech or reading data. Check your state Department of Education website for the specific parental rights notice template and opt-out window, typically ranging from 10 to 30 days.
• Parent opt-out best practice: Districts should provide opt-out forms in the top languages spoken in the community, explain the specific data the tool will collect, and document the alternative assessment pathway the school will use in place of the algorithmic screen.

Actionable takeaways for decision-makers: Before approving any AI reading intervention procurement, request the vendor’s subgroup performance data, pilot the tool with a representative sample of your district’s AAVE-speaking, Appalachian English-speaking, MLL, and IEP populations, build in a human-in-the-loop review where a certified reading specialist or SLP validates every algorithmic referral, and publish a clear parent opt-out procedure that complies with your state’s student-data transparency statute. The algorithm is a powerful early-warning system, but it is not a diagnostic instrument, and the consequences of getting it wrong fall on the children it was designed to help.