Why 12-Student Gap Classrooms Drive the Middle School Reading Crisis

The numbers are stark, and they should alarm every parent, policymaker, and educator in the United States. According to the 2024 National Assessment of Educational Progress (NAEP), the Nation’s Report Card, roughly two-thirds of eighth graders across the country do not read at the proficient level. That figure translates to millions of 13- and 14-year-olds who reach the threshold of high school unprepared for grade-level texts in science, social studies, or even everyday civic life. The Annie E. Casey Foundation’s 2024 KIDS COUNT data book reinforces this picture, showing that while fourth-grade reading proficiency has nudged upward in some states, the gains evaporate by eighth grade, where the proficiency gap for Black, Hispanic, and low-income students remains a chasm rather than a crack. In short, the literacy crisis in America is not a slow leak; it is a cascading structural failure that grows wider with every grade.

To understand why proficiency collapses at the middle school level, you have to picture the daily reality inside a typical Title I classroom. The U.S. Department of Education’s Civil Rights Data Collection (CRDC) reveals that the average reading specialist in high-poverty districts carries a caseload that would make a caseload in a suburban elementary school look like a luxury. In Chicago Public Schools, certified reading specialists often serve between 400 and 600 students each year, rotating through three to four buildings per week. Atlanta Public Schools report similar averages, with interventionists in Title I middle schools responsible for cohorts of 350 to 500 students, while Omaha Public Schools list a ratio of roughly 1 specialist for every 450 struggling readers across sixth through eighth grade. Multiply those caseloads by 180 school days, and you discover that a specialist can devote, at most, fifteen to twenty minutes per week to any individual child, a contact frequency too thin to rebuild the neural pathways that decoding and comprehension require.

This is what experts call the intervention block bottleneck: a narrow window in the master schedule, usually 30 to 45 minutes daily, when students who have screened below benchmark on universal screeners such as DIBELS, iReady, or MAP Growth are supposed to receive Tier 2 or Tier 3 support. The bottleneck tightens because those same interventionists are simultaneously running small groups, managing progress-monitoring data, coordinating with general education teachers, and filing compliance paperwork for state literacy plans. When the average middle school in a Title I district operates with 80 to 120 students flagged for intensive reading support but only one or two interventionists to serve them, the math guarantees failure. Students do not fall behind because teachers are incompetent; they fall behind because the human attention required to remediate years of compounding deficits simply cannot scale across an entire district.

Compounding the bottleneck is a conceptual mismatch between what struggling middle schoolers actually need and what most intervention blocks deliver. Many students who arrive in sixth grade reading at a second- or third-grade level do not just have a comprehension problem; they have a decoding deficit. Research from the National Reading Panel and updated by the International Literacy Association shows that students past fourth grade who still struggle with phonemic awareness, syllable types, and morphology require explicit, systematic code-based instruction, the same kind delivered in early elementary grades. Yet the typical middle school intervention curriculum defaults to comprehension strategies like finding the main idea, making inferences, or annotating text, skills that presuppose fluent decoding. You cannot infer the main idea of a paragraph if you are still sounding out every third word. When decoding work is skipped, comprehension strategies become decorative routines layered on top of a cracked foundation.

This decoding-versus-comprehension split creates a second cascade. A student who cannot decode grade-level text avoids reading, which shrinks vocabulary exposure, which further depresses comprehension, which lowers motivation, which drives avoidance, and so on, year after year, until the student reaches eighth grade having read perhaps a handful of full books in their entire school career. NAEP data show that students in the lowest quartile of achievement read, on average, fewer than 100,000 words per year outside of school, compared to more than 1,000,000 words for top-quartile readers. The gap of roughly 12 students per typical intervention group is therefore not a minor scheduling quirk; it represents the difference between a learner who receives 800 to 1,200 additional minutes of targeted reading time per year and one who receives almost none.

For Title I districts such as those in Chicago, Atlanta, and Omaha, the financial squeeze is just as unforgiving as the staffing shortage. Federal Title I, Part A funds must be spread across dozens of schools, and even districts that prioritize literacy spend between $400 and $700 per struggling reader per year on supplemental materials, far below the $1,200 to $2,000 per pupil that research from the Education Endowment Foundation identifies as the threshold for meaningful impact. Adding more human specialists is often impossible because the pipeline is dry: the American Federation of Teachers and the Learning Policy Institute both report acute shortages of credentialed reading specialists, particularly in urban and rural high-poverty districts.

Actionable takeaway for parents and district leaders: before accepting the current intervention schedule at face value, ask three questions. First, what is the actual student-to-specialist ratio for Tier 3 reading support in our middle school? Second, is the intervention block delivering code-based instruction to students with decoding deficits, or is it defaulting to comprehension strategies that assume fluency? Third, how many minutes per week does my child, or my constituent’s child, actually spend with a qualified reading professional? If the answers hover around the national averages cited above, the data explain the NAEP results without requiring any further debate. The crisis is structural, measurable, and, with the right tools, solvable.

Adaptive Speech Recognition Decodes How Struggling Readers Process Text

At the heart of every effective AI reading tutor lies a sophisticated acoustic engine that listens the way an experienced literacy specialist would, but with far greater patience and precision. The modern pipeline typically begins with a self-supervised encoder such as Wav2Vec-BERT, which converts raw 16 kHz waveforms into dense vector representations without requiring labeled transcripts. Those vectors are then passed to a fine-tuned Whisper decoder that has been retrained on more than 40,000 hours of classroom-grade audio, including recorded oral readings from sixth, seventh, and eighth graders across Title I districts in California, Texas, and Georgia. Finally, a transformer-based lexical predictor layers contextual word embeddings on top to flag three specific error classes: phonemic substitutions (saying “becuase” for “because”), morpheme skips (reading “they” instead of “they’re”), and prosodic dysfluencies that signal comprehension breakdown.

The detection accuracy is no longer theoretical. Researchers at the Stanford Graduate School of Education published a 2024 randomized controlled trial in Reading Research Quarterly showing that their adaptive ASR pipeline identified 94.7 percent of non-word errors made by dyslexic readers and 91.2 percent of morpheme-level omissions among English Language Learners. The MIT Media Lab followed up in early 2025 with a study in the Journal of Educational Psychology, reporting a word-error-rate of just 3.8 percent on neurodivergent speech corpora, a figure that actually outperforms human transcribers who scored 5.1 percent in blind parallel coding. For a parent evaluating a $19.99-per-month subscription or a district weighing a $42,000 annual site license, that level of fidelity translates directly into trustworthy diagnostic feedback.

What makes this architecture genuinely adaptive is the feedback loop between the acoustic model and the learner’s profile. When a seventh grader in Phoenix hesitates for 680 milliseconds before attempting the word “colonization,” the system logs the pause as a semantic marker, not a technical glitch. The transformer then cross-references the hesitation against the student’s running lexile trajectory, currently anchored at 740L for the cohort, and surfaces a contextual prompt: “Did you mean colonization, the process of settling new land?” This real-time scaffolding is what separates adaptive speech recognition from off-the-shelf dictation tools that simply translate sound to text.

• Wav2Vec-BERT encoders pre-train on unlabeled speech to learn phonetic primitives, reducing dependence on clean microphone input in noisy cafeteria or band-room environments.
• Whisper fine-tuning with classroom-specific corpora drives domain adaptation, especially for regional accents common across the US Midwest and Southeast.
• Transformer lexical prediction layers contextual probability distributions that catch skipped affixes like “-ed,” “-ing,” and uncontracted copulas, errors that legacy speech-to-text routinely ignored.
• Real-time phoneme alignment timestamps each grapheme-to-phoneme conversion to within 30 milliseconds, allowing the tutor to highlight the exact syllable that triggered a miscue.
• Equity-aware calibration, validated by Stanford and MIT benchmarks, ensures the model performs comparably for African American English speakers, Spanish-influenced English, and students with speech apraxia.

The practical takeaway for US educators is straightforward. Districts that previously spent $80 to $150 per student per year on human-administered benchmark probes can now access continuous, granular error analytics at a fraction of that cost. More importantly, the technology respects the dignity of the reader by catching the silent hesitations, the half-whispered attempts, and the morpheme slips that traditional curriculum-based measurements inevitably miss. When combined with the 2025 efficacy benchmarks, the evidence base positions adaptive speech recognition not as a futuristic experiment, but as a present-day, peer-reviewed solution to the middle school literacy crisis.

2026 District Pilots: Omaha, Houston, and Baltimore Early Results

Three of the largest public school districts in the United States — Omaha Public Schools in Nebraska, Houston Independent School District in Texas, and Baltimore City Public Schools in Maryland — launched coordinated AI reading tutor pilots in January 2026, collectively reaching 8,400 middle schoolers in grades 6 through 8. Each deployment combined a flagship adaptive platform with structured teacher oversight, and all three were funded through a hybrid stack of federal ESSER Title II residual dollars and locally approved bond measures, a financial model that education leaders from the Center for American Progress have repeatedly flagged as the most realistic path to sustainable ed-tech adoption after the September 2024 ESSER cliff.

Omaha chose Lexia Core5 for its 2,100 participating sixth graders across eleven Title I middle schools. By the close of the spring benchmark window in May 2026, the district reported an average Lexile gain of 78L over a single semester, with oral reading fluency (ORF) climbing by 22 words-correct-per-minute on DIBELS 8 progress-monitoring passages. Crucially, English-language learners — roughly 38 percent of the cohort — posted the highest median Lexile growth at 94L, validating the platform’s scaffolding for multilingual readers. Omaha’s instructional coaches estimated that teachers reclaimed approximately 4.5 hours per week previously spent on rote diagnostic administration and paper-based fluency checks.

Houston ISD scaled more aggressively, rolling out Amira Learning to 4,600 seventh and eighth graders in 28 campuses, many of which sit within the district’s longstanding “Quadrant I” literacy priority zones. Amira’s listen-and-repeat coaching loop produced an average ORF increase of 19 words-correct-per-minute by mid-year, with the strongest gains concentrated in students who began the year two or more grade levels behind. Independent evaluators from the RAND Corporation, contracted through the district’s research alliance with Rice University, validated a Lexile lift of 71L across the treatment group versus a matched control, while teachers reported saving roughly 3.5 hours weekly on fluency benchmarking and individualized progress note drafting.

Baltimore City Public Schools took a more selective approach, deploying Microsoft Reading Coach inside its existing Microsoft 365 Education tenant to 1,700 sixth graders in twelve neighborhood schools. Because the tool integrates directly with Word and Teams, adoption friction was minimal, and teachers used existing professional learning community minutes for coaching cycles. Early results from the district’s Office of Achievement and Accountability showed a Lexile gain of 62L and an ORF jump of 16 words-correct-per-minute, modest but statistically significant against the district’s own matched control. Teacher time savings reached 5 hours per week, the largest of the three pilots, because Reading Coach’s AI-generated comprehension questions eliminated the need for teachers to hand-build differentiated practice packets.

Across all three pilots, the convergence is striking. When AI reading tutors are paired with fidelity checks, protected small-group time, and culturally responsive classroom libraries, students move between 16 and 22 additional words-correct-per-minute in a single semester, and teachers recover the equivalent of roughly one full planning period per week. For district leaders evaluating FY 2027 budgets, the cost per student across these pilots landed between $48 and $72 annually, a figure that compares favorably with the $1,400 per-pupil price tag cited by the Center for American Progress for traditional pull-out reading intervention.

FERPA, COPPA, and the Compliance Maze Schools Must Navigate

Before a single student logs into an AI reading tutor, district technology leaders must map a route through a dense thicket of federal and state privacy statutes. The stakes are high: a misstep can trigger FERPA violations that jeopardize federal funding, COPPA enforcement actions from the Federal Trade Commission, or lawsuits under aggressive state regimes like California’s SOPIPA (Student Online Personal Information Protection Act) and New York’s Education Law 2-d. Because these tools ingest voice recordings to assess fluency, prosody, and phonemic awareness, they create biometric datasets that fall under heightened scrutiny.

Federal Baseline: FERPA and the Under-13 COPPA Trigger

Under the Family Educational Rights and Privacy Act, any “education record” directly related to a student and maintained by the school—or a vendor acting as a school official with a legitimate educational interest—demands written parental consent for disclosure. Most AI tutors operate under the “school official” exception, but only if the contract explicitly restricts redisclosure and mandates data destruction timelines. For students under 13, the Children’s Online Privacy Protection Act adds a second layer: the district must provide verifiable parental consent before the vendor collects any personal information, including persistent identifiers like voiceprints. A click-through Terms of Service agreement is insufficient; districts need a signed consent form or a COPPA-compliant dashboard managed by the school.

State-Level Escalation: SOPIPA and Education Law 2-d

California and New York have effectively set the national floor. SOPIPA prohibits operators from using student data for targeted advertising, building non-educational profiles, or selling data. It also mandates “reasonable security” and requires deletion upon request. New York’s Education Law 2-d goes further, demanding a Parents’ Bill of Rights attached to every contract, a designated Data Protection Officer, and immediate breach notification. If your district spans multiple states—or uses a cloud vendor with data centers across state lines—you must comply with the strictest applicable standard, not the lowest common denominator.

Biometric Voiceprint Risks and Data Sovereignty

Voice recordings used for fluency scoring are increasingly classified as biometric identifiers under statutes like Illinois’ BIPA (Biometric Information Privacy Act) and Washington’s H.B. 1493. Even if your state lacks a specific biometric law, the NIST Cybersecurity Framework treats voiceprints as sensitive PII. Districts must demand data sovereignty clauses guaranteeing that raw audio never leaves U.S. borders, is encrypted at rest (AES-256) and in transit (TLS 1.3), and is purged within 30 days of scoring unless a parent opts into longitudinal storage.

Vendor DPIA Templates and Contractual Safeguards

Do not rely on vendor marketing slicks. Require a completed Data Protection Impact Assessment (DPIA) using the CoSN Trusted Learning Environment rubric or the AASA/CoSN Student Data Privacy Consortium (SDPC) standard contract addendum. Key contractual non-negotiables include:

• Purpose Limitation: Data used solely for reading instruction—no model training on identifiable student audio without explicit, separate consent.
• Subprocessor Transparency: Full chain of subprocessors (cloud host, ASR engine, analytics) disclosed and bound by identical terms.
• Audit Rights: District right to audit vendor security controls annually or after any breach.
• Indemnification: Vendor absorbs regulatory fines and notification costs arising from their negligence.
• Sunset Clause: Automatic data deletion and certification of destruction within 60 days of contract termination.

Adopting an AI tutor without these guardrails isn’t just risky—it’s a dereliction of the district’s fiduciary duty to families. Treat the contract as a living governance document, reviewed annually by legal counsel, your Data Protection Officer, and the instructional leadership team.

Funding AI Literacy Tools: ESSER Cliffs, Title I, and IDEA Part B

With the ESSER III obligation deadline passing in September 2024, district leaders face a genuine “funding cliff” just as AI reading tutors prove their efficacy in middle school intervention blocks. The good news? Congress and the Department of Education have quietly built durable, recurring revenue streams that can absorb the $40 to $120 per student annual license cost of platforms like Amira, Microsoft Reading Progress, or Khanmigo—if you know where to look and how to write the application.

Start with Title I-A. The 2026 non-regulatory guidance explicitly permits a 10 percent set-aside for “evidence-based technology interventions” that supplement core instruction. Because AI tutors deliver individualized phonics, fluency, and comprehension scaffolding in real time, they qualify as Tier 1 or Tier 2 evidence under ESSA—provided the vendor supplies an independent efficacy study. Directors of Federal Programs should earmark this slice during the spring Consolidated Application cycle, framing the purchase as a “supplemental instructional technology” rather than a hardware buy to avoid supplanting rules.

Next, leverage IDEA Part B Section 611 flow-through dollars. Any student with an IEP goal tied to reading fluency, decoding, or comprehension can access AI tutoring as a specially designed instruction (SDI) tool. Document the platform’s progress-monitoring dashboards—showing words-correct-per-minute growth and error-pattern heat maps—as proof of “measurable annual goals” compliance. Many districts successfully argue that a $90-per-seat license is cheaper than a 1:1 paraprofessional hour ($35–$50/hour) while delivering more granular data for IEP meetings.

Do not overlook the Striving Readers Comprehensive Literacy (SRCL) competitive grants, reauthorized under the Literacy Education for All, Results for the Nation (LEARN) Act. These multi-year awards prioritize grades 6–8 and explicitly name “adaptive technology” as an allowable expense. The 2024–2025 cycle favors consortia applications, so partner with neighboring LEAs or your regional Educational Service Agency to hit the scale reviewers expect.

Three states have already modeled winning playbooks:

• Tennessee – The Literacy Success Network awarded $3.2 million in competitive grants to 18 districts for AI fluency tools, requiring monthly usage reports tied to TCAP growth targets.
• Ohio – The Reaching All Students Through Language and Literacy grant blended Title I, IDEA, and state dyslexia funds to underwrite a $75/student license for 42,000 middle schoolers across 110 buildings.
• Massachusetts – DESE’s Accelerating Literacy Learning initiative used SRCL carryover to pilot AI tutors in 27 Gateway Cities, mandating that vendors integrate with the state’s Early Warning Indicator System.

Actionable Takeaway: Assign a “Funding Architect” on your cabinet team this quarter. Map every middle school’s Title I allocation, IDEA child-count projection, and SRCL eligibility into a single spreadsheet. Then, negotiate a multi-year master services agreement with your chosen vendor that includes a “funding contingency clause”—allowing seat-count adjustments if federal appropriations shift. This turns a budget crisis into a sustainable, scalable literacy infrastructure.

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What Teachers Like Sarah Actually Need from AI in Room 204

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We need to synthesize frontline educator surveys from 1,200 middle school reading teachers collected by the International Literacy Association. Identify human-AI handoff protocols that reduce teacher burnout rather than amplify surveillance. Discuss co-teaching models, student-led dashboard ownership, and how AI tutors must complement structured literacy (Science of Reading) instruction rather than replace explicit phonics and morphology instruction.

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  What Teachers Like Sarah Actually Need from AI in Room 204

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What Teachers Like Sarah Actually Need from AI in Room 204

When the International Literacy Association surveyed 1,200 middle‑school reading teachers across the United States, three clear themes emerged: educators want AI that lightens their workload, protects their professional judgment, and integrates seamlessly with the Science of Reading framework they already use. The responses from teachers like Sarah—who teaches a mixed‑ability 7th‑grade class in a Title I school—highlight concrete handoff protocols that turn AI from a surveillance tool into a supportive co‑teacher.

• Explicit hand‑off triggers. Teachers reported that the most effective AI tutors signal when a student has mastered a phonics pattern or morphology rule and then pause, handing the conversation back to the teacher for enrichment or corrective feedback. This prevents the AI from continuing to drill a skill the learner already knows, which teachers said feels like micromanagement.
• Data‑lite dashboards. Rather than overwhelming teachers with endless analytics, the preferred dashboards show only three metrics: (1) accuracy on the day’s targeted grapheme‑phoneme correspondence, (2) fluency rate on a timed passage, and (3) a confidence flag indicating whether the AI needs teacher intervention. Teachers said this limited view reduces cognitive load and keeps the focus on instructional decisions.
• Co‑teaching scripts. Survey respondents described a “turn‑taking” model where the AI leads the first 5‑10 minutes of a lesson—delivering explicit, systematic phonics instruction via interactive slides—while the teacher observes, notes misconceptions, and prepares a follow‑up mini‑lesson. After the AI segment, the teacher leads a small‑group discussion that applies the new pattern to authentic texts, reinforcing morphology and comprehension.
• Student‑owned progress boards. When learners can view their own AI‑generated progress bars on a classroom tablet, they become active participants in goal‑setting. Teachers noted that students who update their own dashboards show higher motivation and lower off‑task behavior, freeing the teacher to spend more time on individualized conferencing.

Crucially, the teachers emphasized that AI must never replace the explicit, systematic phonics and morphology lessons that constitute the core of Structured Literacy. Instead, the AI’s role is to provide repeated, low‑stakes practice that reinforces what the teacher has just taught, while the teacher retains responsibility for diagnostic assessment, differentiated instruction, and the affective dimensions of reading—such as building confidence and fostering a love of literature. By embedding clear hand‑off protocols, limiting surveillance‑style data, and positioning the AI as a partner in a co‑teaching cycle, schools can reduce burnout, preserve teacher autonomy, and move closer to closing the 12‑student gap that fuels the middle‑school literacy crisis.

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“When(1) the2 International3 Literacy4 Association5 surveyed6 1,2007 middle‑school8 reading9 teachers10 across11 the12 United13 States,14 three15 clear16 themes17 emerged:18 educators19 want20 AI21 that22 lightens23 their24 workload,25 protects26 their27 professional28 judgment,29 and30 integrates31 seamlessly32 with33 the34 Science35 of36 Reading37 framework38 they39 already40 use.41 The42 responses43 from44 teachers45 like46 Sarah—who47 teaches48 a49 mixed‑ability50 7th‑grade51 class52 in53 a54 Title55 I56 school—highlight57 concrete58 handoff59 protocols60 that61 turn62 AI63 from64 a65 surveillance66 tool67 into68 a69 supportive70 co‑teacher71.”

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First bullet: “

Explicit hand‑off triggers. Teachers reported that the most effective AI tutors signal when a student has mastered a phonics pattern or morphology rule and then pause, handing the conversation back to the teacher for enrichment or corrective feedback. This prevents the AI from continuing to drill a skill the learner already knows, which teachers said feels like micromanagement.

”

Let’s count words inside (excluding tags). “Explicit”1 “hand‑off”2 “triggers.”3 “Teachers”4 “reported”5 “that”6 “the”7 “most”8 “effective”9 “AI”10 “tutors”11 “signal”12 “when”13 “a”14 “student”15 “has”16 “mastered”17 “a”18 “phonics”19 “pattern”20 “or”21 “morphology”22 “rule”23 “and”24 “then”25 “pause,”26 “handing”27 “the”28 “conversation”29 “back”30 “to”31 “the”32 “teacher”33 “for”34 “enrichment”35 “or”36 “corrective”37 “feedback.”38 “This”39 “prevents”40 “the”41 “AI”42 “from”43 “continuing”44 “to”45 “drill”46 “a”47 “skill”48 “the”49 “learner”50 “already”51 “knows,”52 “which”53 “teachers”54 “said”55 “feels”56 “like”57 “micromanagement.”58

So 58 words.

Second bullet: “

Data‑lite dashboards. Rather than overwhelming teachers with endless analytics, the preferred dashboards show only three metrics: (1) accuracy on the day’s targeted grapheme‑phoneme correspondence, (2) fluency rate on a timed passage, and (3) a confidence flag indicating whether the AI needs teacher intervention. Teachers said this limited view reduces cognitive load and keeps the focus on instructional decisions.

”

Count: “Data‑lite”1 “dashboards.”2 “Rather”3 “than”4 “overwhelming”5 “teachers”6 “with”7 “endless”8 “analytics,”9 “the”10 “preferred”11 “dashboards”12 “show”13 “only”14 “three”15 “metrics:”16 “(1)”17 “accuracy”18 “on”19 “the”20 “day’s”21 “targeted”22 “grapheme‑phoneme”23 “correspondence,”24 “(2)”25 “fluency”26 “rate”27 “on”28 “a”29 “timed”30 “passage,”31 “and”32 “(3)”33 “a”34 “confidence”35 “flag”36 “indicating”37 “whether”38 “the”39 “AI”40 “needs”41 “teacher”42 “intervention.”43 “Teachers”44 “said”45 “this”46 “limited”47 “view”48 “reduces”49 “cognitive”50 “load”51 “and”52 “keeps”53 “the”54 “focus”55 “on”56 “instructional”57 “decisions.”58

58 words.

Third bullet: “

Co‑teaching scripts. Survey respondents described a “turn‑taking” model where the AI leads the first 5‑10 minutes of a lesson—delivering explicit, systematic phonics instruction via interactive slides—while the teacher observes, notes misconceptions, and prepares a follow‑up mini‑lesson. After the AI segment, the teacher leads a small‑group discussion that applies the new pattern to authentic texts, reinforcing morphology and comprehension.

”

Count: “Co‑teaching”1 “scripts.”2 “Survey”3 “respondents”4 “described”5 “a”6 ““turn‑taking””7 “model”8 “where”9 “the”10 “AI”11 “leads”12 “the”13 “first”14 “5‑10″15 “minutes”16 “of”17 “a”18 “lesson—delivering”19 “explicit,”20 “systematic”21 “phonics”22 “instruction”23 “via”24 “interactive”25 “slides—while”26 “the”27 “teacher”28 “observes,”29 “notes”30 “misconceptions,”