The science of reading revolution has the right science—but the wrong system

Editor’s note: This essay is an entry in Fordham’s 2025 Wonkathon, which asked contributors to answer this question: “What needs to happen next—at the state, district, and school levels—for the science of reading revolution to fulfill its promise and ensure that far more children learn to read well?” Learn more.

The science of reading (SoR) revolution has achieved something rare in education: bipartisan consensus and measurable shifts in policy and practice. As of October 2025, forty states have enacted legislation requiring evidence-based reading instruction, teacher preparation programs are being retooled, and publishers have developed high-quality instructional materials (HQIM) aligned to the research base.

Yet progress has not consistently followed policy enactment. Too many classrooms still struggle to translate the science into consistent results. Teachers know what to do but lack the time, structure, and support to do it for every child. In most K–2 classrooms, one teacher is responsible for diagnosing, instructing, differentiating, and intervening with twenty or more students—each on a unique literacy trajectory.

If the SoR movement is to fulfill its promise, we must modernize the delivery model of early literacy. We must move from solo teaching practice to team-based teaching practice, pairing high-quality curriculum and emerging AI tools with the human capacity to use them well.

A mismatch between science and structure

The SoR tells us that effective reading instruction requires explicit, systematic teaching of foundational skills; frequent progress monitoring; and differentiated support for students who struggle to master those foundations. Each of these elements takes time, educator skill, and sustained attention.

But the structure of most early elementary classrooms—one teacher, one room, twenty-five (plus or minus five or so) learners—was never designed for such precision. It is as if we asked a single physician to treat thirty patients at once, all with different needs and rates of recovery.

This is not a question of teacher effort; it is a question of system design. The next chapter of the SoR revolution must be about re-engineering the conditions under which literacy instruction happens.

The case for team-based models

Team-based models distribute the work of teaching across multiple adults who collaborate around a shared group of students. A well-structured literacy team can include:

• Lead teachers who design and oversee the instructional arc, ensuring fidelity to the science and coherence across lessons;
• Certified educators or interventionists who provide small-group or Tier II supports;
• Paraeducators and high-impact tutors who deliver targeted practice using HQIM-aligned materials;
• High school students in CTE Education Professions programs, trained in the SoR, who support early learners while gaining hands-on experience and exposure to teaching careers.

Together, these teams can create instructional capacity that mirrors what the SoR requires—consistent, data-informed teaching with frequent opportunities for feedback and reinforcement.

A single teacher can implement elements of the science of reading. A team can implement all of it, for every child.

Integrating HQIM: The curriculum infrastructure

High-quality instructional materials (HQIM) aligned to the science of reading are the backbone of implementation. They provide the “recipe” and sequence—structured phonics, decodable texts, built-in assessments, and aligned supports—that ensure consistency across classrooms and teachers.

But HQIM alone are not enough. Even the best materials fail without the time, coordination, and shared expertise that team-based models provide. Conversely, team-based models without HQIM risk inconsistency and improvisation.

The most effective systems will integrate team-based staffing with HQIM so that each member of the team plays a distinct, complementary role:

• Lead teachers and other certified educators internalize and model lessons directly from the HQIM scope and sequence.
• Paraeducators, high-impact tutors and high school CTE students deliver intervention and practice sessions using decodables and routines drawn directly from the same materials.
• Interventionists monitor data dashboards embedded within HQIM platforms to track student growth and identify additional support needed.

Lead teachers also have a key role to play in supporting the training and implementation of team-based models.

When everyone works from the same instructional blueprint, coherence replaces fragmentation, and powerful instruction becomes possible.

Leveraging AI tools powered by HQIM

Artificial intelligence, when carefully designed and paired with HQIM, offers a promising next step. Rather than replacing teachers, AI can act as an amplifier of high-quality instruction. We anticipate the most promising tools to support improved student outcomes will be the ones that:

• Provide real-time progress monitoring and speech recognition to assess phonemic awareness and decoding accuracy;
• Suggest targeted small-group or one-on-one interventions using formative data;
• Generate customized practice activities aligned to the specific phonics patterns or comprehension skills being taught;
• Support tutors and paraeducators by providing just-in-time guidance on prompts, corrections, and pacing.

AI tools, powered by research-aligned content, give teams the diagnostic precision the SoR demands, and with team-based models, data becomes actionable: one adult can monitor progress while another adjusts instruction in real time.

What needs to happen next

At the state level: Build capacity, not just compliance

1. Fund staffing innovation grants. States should pilot and scale team-based literacy models that integrate SoR-aligned HQIM and AI tools.  
2. Incentivize adoption of HQIM ecosystems. Move beyond material reviews to create marketplaces of approved HQIM that include digital and AI-enabled tools vetted for research alignment and privacy standards.  
3. Create flexible licensure pathways. Allow paraeducators, tutors, and CTE students to earn micro-credentials in early literacy practices aligned to the SoR, building a talent pipeline for future teachers.  
4. Measure implementation quality. Develop indicators that track not only whether districts have adopted the right curriculum but also whether they have the team-based staffing and professional learning structures to deliver it.

At the district level: Align structures and supports

1. Redesign schedules for collaboration. Protect shared planning and reflection time for teams to review literacy data, co-plan lessons, and coordinate interventions.  
2. Reallocate funding toward team staffing. Use state innovation grants or Title funds to provide lead teacher stipends and to staff reading specialists, tutors, and paraeducators strategically rather than uniformly across schools.  
3. Integrate data systems. Connect HQIM platforms, progress monitoring tools, and AI dashboards into a single view so educator teams can respond to data together.  
4. Invest in ongoing professional learning. Focus on practice-based coaching tied directly to the district’s HQIM and the specific routines used in team-based literacy blocks.

At the school level: Create literacy teams

1. Establish enabling conditions. Principals should act as “chief literacy officers,” ensuring that time, staffing, and professional learning align to the science of reading rather than to tradition or convenience.  
2. Form dedicated literacy teams for K–2. Each team should include a lead teacher and other certified educators – including a special education teacher – on the core team, with paraeducators, high-impact tutors, and/or CTE Ed Professions students contributing as extended team members.  
3. Define clear roles and routines. Each adult contributes differently—small-group instruction, progress monitoring, phonics practice, comprehension discussions—but all work from the same HQIM and student data.  
4. Use AI-informed data to drive instruction. Weekly team meetings should review dashboards from HQIM and aligned AI tools, identifying which students need what support next.

Conclusion: Building the system our children deserve

The science of reading revolution has given us the “what” and the “why.” The next phase must focus on the “how.”

We must modernize the structure of early literacy instruction. The science is ready. The tools are ready. The next revolution is about teams who provide care.