Tag: need

  • What school leaders need to know

    What school leaders need to know

    Key points:

    Special education is at a breaking point. Across the country, more children than ever are being referred for evaluations to determine whether they qualify for special education services. But there aren’t enough school psychologists or specialists on staff to help schools meet the demand, leaving some families with lengthy wait times for answers and children missing critical support. 

    The growing gap between need and capacity has inspired districts to get creative. One of the most debated solutions? Remote psychoeducational testing, or conducting evaluations virtually rather than face-to-face. 

    Can a remote evaluation accurately capture what a child needs? Will the results hold up if challenged in a legal dispute? Is remote assessment equivalent to in-person? 

    As a school psychologist and educational consultant, I hear these questions every week. And now, thanks to research and data released this summer, I can answer with confidence: Remote psychoeducational testing can produce equivalent results to traditional in-person assessment. 

    What the research shows

    In July 2025, a large-scale national study compared in-person and remote administration of the Woodcock-Johnson V Tests of Cognitive Abilities and Achievement (WJ V), the latest version of one of the most widely-used and comprehensive assessment systems for evaluating students’ intellectual abilities, academic achievement, and oral language skills. Using a matched case-control design with 300 participants and 44 licensed school psychologists from across the U.S., the study found no statistically or practically significant difference in student scores between in-person and remote formats. 

    In other words: When conducted with fidelity, remote WJ V testing produces equivalent results to traditional in-person assessment.

    This study builds on nearly a decade of prior research that also found score equivalency for remote administrations of the most widely used evaluations including WJ IV COG and ACH, RIAS-2, and WISC-V assessments, respectively. 

    The findings of the newest study are as important as they are urgent. They show remote testing isn’t just a novelty–it’s a practical, scalable solution that is rooted in evidence. 

    Why it matters now

    School psychology has been facing a workforce shortage for over a decade. A 2014 national study predicted this crunch, and today districts are relying on contracting agencies and remote service providers to stay afloat. At the same time, referrals for evaluations are climbing, driven by pandemic-related learning loss, growing behavioral challenges, and increased awareness of neurodiversity. 

    The result: More children and families waiting longer for answers, while school psychologists are facing mounting caseloads and experiencing burnout. 

    Remote testing offers a way out of this cycle and embraces changes. It allows districts to bring in licensed psychologists from outside their area, without relocating staff or asking families to travel. It helps schools move through backlogs more efficiently, ensuring students get the services they need sooner. And it gives on-site staff space to do the broader preventative work that too often gets sidelined. Additionally, it offers a way to support those students who are choosing alternate educational settings, such as virtual schools. 

    Addressing the concerns

    Skepticism remains, and that’s healthy. Leaders wonder: Will a hearing officer accept remote scores in a due process case? Are students disadvantaged by the digital format? Can we trust the results to guide placement and services?

    These are valid questions, but research shows that when remote testing is done right, the results are valid and reliable. 

    Key phrase: Done right. Remote assessment isn’t just a Zoom call with a stopwatch. In the most recent study, the setup included specific safeguards:

    • Touchscreen laptops with screens 13” or larger; 
    • A secure platform with embedded digital materials;
    • Dual cameras to capture the student’s face and workspace;
    • A guided proctor in-room with the student; and
    • Standardized examiner and proctor training protocols.

    This carefully structured environment replicates traditional testing conditions as closely as possible. All four of the existing equivalency studies utilized the Presence Platform, as it already meets with established criteria.

    When those fidelity conditions are met, the results hold up. Findings showed p-values above .05 and effect sizes below .03 across all tested subtests, indicating statistical equivalence. This means schools can confidently use WJ V scores from remote testing, provided the setup adheres to best practices.

    What district leaders can do

    For remote testing to succeed, schools need to take a thoughtful, structured approach. Here are three steps districts can take now.

    1. Vet providers carefully. Ask about their platform, equipment, training, and how they align with published research standards. 
    2. Clarify device requirements. Ensure schools have the right technology in place before testing begins.
    3. Build clear policies. Set district-wide expectations for how remote testing should be conducted so everyone–staff and contractors alike–are on the same page. 

    A path forward

    Remote assessment won’t solve every challenge in special education, but it can close one critical gap: timely, accurate evaluations. For students in rural districts, schools with unfilled psychologist positions, virtual school settings, or families tired of waiting for answers, it can be a lifeline.

    The research is clear. Remote psychoeducational testing works when we treat it with the same care and rigor as in-person assessment. The opportunity now is to use this tool strategically–not as a last resort, but as part of a smarter, more sustainable approach to serving students. 

    At its best, remote testing is not a compromise; it’s a path toward expanded access and stronger support for the students who need it most.

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  • AI use is on the rise, but is guidance keeping pace?

    AI use is on the rise, but is guidance keeping pace?

    Key points:

    The rapid rise of generative AI has turned classrooms into a real-time experiment in technology use. Students are using AI to complete assignments, while teachers are leveraging it to design lessons, streamline grading, and manage administrative tasks.

    According to new national survey data from RAND, AI use among both students and educators has grown sharply–by more than 15 percentage points in just the past one to two years. Yet, training and policy have not kept pace. Schools and districts are still developing professional development, student guidance, and clear usage policies to manage this shift.

    As a result, educators, students, and parents are navigating both opportunities and concerns. Students worry about being falsely accused of cheating, and many families fear that increased reliance on AI could undermine students’ critical thinking skills.

    Key findings:

    During the 2024-2025 school year, AI saw rapid growth.

    AI use in schools surged during the 2024-2025 academic year. By 2025, more than half of students (54 percent) and core subject teachers (53 percent) were using AI for schoolwork or instruction–up more than 15 points from just a year or two earlier. High school students were the most frequent users, and AI adoption among teachers climbed steadily from elementary to high school.

    While students and parents express significant concern about the potential downsides of AI, school district leaders are far less worried.

    Sixty-one percent of parents, 48 percent of middle school students, and 55 percent of high school students believe that increased use of AI could harm students’ critical-thinking skills, compared with just 22 percent of district leaders. Additionally, half of students said they worry about being falsely accused of using AI to cheat.

    Training and policy development have not kept pace with AI use in schools.

    By spring 2025, only 35 percent of district leaders said their schools provide students with training on how to use AI. Meanwhile, more than 80 percent of students reported that their teachers had not explicitly taught them how to use AI for schoolwork. Policy guidance also remains limited–just 45 percent of principals said their schools or districts have policies on AI use, and only 34 percent of teachers reported policies specifically addressing academic integrity and AI.

    The report offers recommendations around AI use and guidance:

    As AI technology continues to evolve, trusted sources–particularly state education agencies–should provide consistent, regularly updated guidance on effective AI policies and training. This guidance should help educators and students understand how to use AI as a complement to learning, not a replacement for it.

    District and school leaders should clearly define what constitutes responsible AI use versus academic dishonesty and communicate these expectations to both teachers and students. In the near term, educators and students urgently need clarity on what qualifies as cheating with AI.

    Elementary schools should also be included in this effort. Nearly half of elementary teachers are already experimenting with AI, and these early years are when students build foundational skills and habits. Providing age-appropriate, coherent instruction about AI at this stage can reduce misuse and confusion as students progress through school and as AI capabilities expand.

    Ultimately, district leaders should develop comprehensive AI policies and training programs that equip teachers and students to use AI productively and ethically across grade levels.

    Laura Ascione
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  • Solving the staffing crisis is key to the Science of Reading movement

    Solving the staffing crisis is key to the Science of Reading movement

    Key points:

    As someone who’s dedicated my career to advancing the Science of Reading movement, I’ve seen firsthand what it takes to help every child become a strong, fluent reader. We’ve made incredible strides in shifting the conversation toward evidence-based instruction, but I know we’re at a critical inflection point. While we–obviously–continue our work helping schools and districts adopt SOR, there’s an issue that stands in the way of real, sustained, progress: the staffing crisis and leadership churn that are leaving our educators overwhelmed and skeptical toward “change.” Without addressing these deeper structural issues, we risk stalling the momentum we’ve worked so hard to build.

    The hidden costs of constant turnover

    The data on teacher and leader turnover is bleak, and I’ve seen how it undermines the long-term commitment needed for any meaningful change. Consider this: Roughly 1 in 6 teachers won’t return to the same classroom next year, and nearly half of new teachers leave within their first five years. This constant churn is a massive financial burden on districts, costing an estimated $20,000 per teacher to recruit, hire, and onboard. But the real cost is the human one. Every time a new leader or teacher steps in, the hard-won progress on a literacy initiative can be jeopardized.

    I’ve watched districts spend years building momentum for the Science of Reading, providing extensive training and resources, only to see a new superintendent or principal arrive with a new set of priorities. This “leader wobble” can pull the rug out from under an initiative mid-stream. It’s especially frustrating when a new leader decides a program has had “plenty of professional learning” without taking the time to audit its impact. This lack of continuity completely disrupts the 3-5 years it takes for an initiative to truly take hold, especially because new teachers often arrive with a knowledge gap, as only about one-quarter of teacher preparation programs teach the Science of Reading. We can’t build on a foundation that’s constantly shifting.

    Overwhelmed by “initiative fatigue”

    I know what it feels like to have too much on your plate. Teachers, already juggling countless instructional materials, often see each new program not as a solution but as one more thing to learn, implement, and manage. Instead of excitement, there’s skepticism–this is initiative fatigue, and it can stall real progress. I’ve seen it firsthand; one large district I worked with rolled out new reading, math, and phonics resources all at once.

    To prevent this, we need to follow the principle of “pull weeds to plant flowers.” Being critical, informed consumers of resources means choosing flowers (materials) that are:

    • Supported by high-quality, third-party research
    • Aligned across all tiers of instruction
    • Versatile enough to meet varied student needs
    • Teacher-friendly, with clear guidance and instructional dialogue
    • Culturally relevant, reflecting the diverse backgrounds of students

    Now, even when a resource meets these standards, adoption shouldn’t be additive. Teachers can’t layer new tools on top of old ones. To see real change, old resources must be replaced with better ones. Educators need solutions that provide a unified, research-backed framework across all tiers, giving teachers clarity, support, and a path to sustainable student progress.

    Building a stable environment for sustained change

    So, how do we create the stable environment needed to support our educators? It starts with leadership that is in it for the long game. We need to mitigate turnover by using data to understand why teachers are leaving and then acting on that feedback. Strengthening mentorship, clarifying career pathways, and improving school culture are all crucial steps.

    Beyond just retaining staff, leaders must foster a culture of sustained commitment. It’s not enough to have a few “islands of excellence” where a handful of teachers are getting great results.

    We need system-wide adoption. This requires strong leaders to balance support and accountability. I’ve seen how collaborative teams, engaged in problem-solving and data-based decision-making, can transform a school. When teachers see students as “our students” and not just “my students,” shared ownership grows.

    A leader’s job is to protect and sustain this vision, making sure the essential supports–like collaborative planning time, ongoing professional development, and in-classroom coaching–are in place. But sustaining change goes beyond daily management; it requires building deep capacity so the work continues even if leadership shifts. This means hiring, training, and retaining strong educators, investing in future leaders, and ensuring committed advocates are part of the implementation team. It also requires creating a detailed, actionable roadmap, with budgets clearly allocated and accountability measures established, so that any initiative isn’t just a short-term priority but a long-term promise. By embedding these structures, leaders can secure continuity, maintain momentum, and ensure that every step forward in literacy translates into lasting gains for students.

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  • Students must intentionally develop durable skills to thrive in an AI-dominated world

    Students must intentionally develop durable skills to thrive in an AI-dominated world

    Key points:

    As AI increasingly automates technical tasks across industries, students’ long-term career success will rely less on technical skills alone and more on durable skills or professional skills, often referred to as soft skills. These include empathy, resilience, collaboration, and ethical reasoning–skills that machines can’t replicate.

    This critical need is outlined in Future-Proofing Students: Professional Skills in the Age of AI, a new report from Acuity Insights. Drawing on a broad body of academic and market research, the report provides an analysis of how institutions can better prepare students with the professional skills most critical in an AI-driven world.

    Key findings from the report:

    • 75 percent of long-term job success is attributed to professional skills, not technical expertise.
    • Over 25 percent of executives say they won’t hire recent graduates due to lack of durable skills.
    • COVID-19 disrupted professional skill development, leaving many students underprepared for collaboration, communication, and professional norms.
    • Eight essential durable skills must be intentionally developed for students to thrive in an AI-driven workplace.

    “Technical skills may open the door, but it’s human skills like empathy and resilience that endure over time and lead to a fruitful and rewarding career,” says Matt Holland, CEO at Acuity Insights. “As AI reshapes the workforce, it has become critical for higher education to take the lead in preparing students with these skills that will define their long-term success.”

    The eight critical durable skills include:

    • Empathy
    • Teamwork
    • Communication
    • Motivation
    • Resilience
    • Ethical reasoning
    • Problem solving
    • Self-awareness

    These competencies don’t expire with technology–they grow stronger over time, helping graduates adapt, lead, and thrive in an AI-driven world.

    The report also outlines practical strategies for institutions, including assessing non-academic skills at admissions using Situational Judgment Tests (SJTs), and shares recommendations on embedding professional skills development throughout curricula and forming partnerships that bridge AI literacy with interpersonal and ethical reasoning.

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  • Preparing for a new era of teaching and learning

    Preparing for a new era of teaching and learning

    Key points:

    When I first started experimenting with AI in my classroom, I saw the same thing repeatedly from students. They treated it like Google. Ask a question, get an answer, move on. It didn’t take long to realize that if my students only engage with AI this way, they miss the bigger opportunity to use AI as a partner in thinking. AI isn’t a magic answer machine. It’s a tool for creativity and problem-solving. The challenge for us as educators is to rethink how we prepare students for the world they’re entering and to use AI with curiosity and fidelity.

    Moving from curiosity to fluency

    In my district, I wear two hats: history teacher and instructional coach. That combination gives me the space to test ideas in the classroom and support colleagues as they try new tools. What I’ve learned is that AI fluency requires far more than knowing how to log into a platform. Students need to learn how to question outputs, verify information and use results as a springboard for deeper inquiry.

    I often remind them, “You never trust your source. You always verify and compare.” If students accept every AI response at face value, they’re not building the critical habits they’ll need in college or in the workforce.

    To make this concrete, I teach my students the RISEN framework: Role, Instructions, Steps, Examples, Narrowing. It helps them craft better prompts and think about the kind of response they want. Instead of typing “explain photosynthesis,” they might ask, “Act as a biologist explaining photosynthesis to a tenth grader. Use three steps with an analogy, then provide a short quiz at the end.” Suddenly, the interaction becomes purposeful, structured and reflective of real learning.

    AI as a catalyst for equity and personalization

    Growing up, I was lucky. My mom was college educated and sat with me to go over almost every paper I wrote. She gave me feedback that helped to sharpen my writing and build my confidence. Many of my students don’t have that luxury. For these learners, AI can be the academic coach they might not otherwise have.

    That doesn’t mean AI replaces human connection. Nothing can. But it can provide feedback, ask guiding questions, and provide examples that give students a sounding board and thought partner. It’s one more way to move closer to providing personalized support for learners based on need.

    Of course, equity cuts both ways. If only some students have access to AI or if we use it without considering its bias, we risk widening the very gaps we hope to close. That’s why it’s our job as educators to model ethical and critical use, not just the mechanics.

    Shifting how we assess learning

    One of the biggest shifts I’ve made is rethinking how I assess students. If I only grade the final product, I’m essentially inviting them to use AI as a shortcut. Instead, I focus on the process: How did they engage with the tool? How did they verify and cross-reference results? How did they revise their work based on what they learned? What framework guided their inquiry? In this way, AI becomes part of their learning journey rather than just an endpoint.

    I’ve asked students to run the same question through multiple AI platforms and then compare the outputs. What were the differences? Which response feels most accurate or useful? What assumptions might be at play? These conversations push students to defend their thinking and use AI critically, not passively.

    Navigating privacy and policy

    Another responsibility we carry as educators is protecting our students. Data privacy is a serious concern. In my school, we use a “walled garden” version of AI so that student data doesn’t get used for training. Even with those safeguards in place, I remind colleagues never to enter identifiable student information into a tool.

    Policies will continue to evolve, but for day-to-day activities and planning, teachers need to model caution and responsibility. Students are taking our lead.

    Professional growth for a changing profession

    The truth of the matter is most of us have not been professionally trained to do this. My teacher preparation program certainly did not include modules on prompt engineering or data ethics. That means professional development in this space is a must.

    I’ve grown the most in my AI fluency by working alongside other educators who are experimenting, sharing stories, and comparing notes. AI is moving fast. No one has all the answers. But we can build confidence together by trying, reflecting, and adjusting through shared experience and lessons learned. That’s exactly what we’re doing in the Lead for Learners network. It’s a space where educators from across the country connect, learn and support one another in navigating change.

    For educators who feel hesitant, I’d say this: You don’t need to be an expert to start. Pick one tool, test it in one lesson, and talk openly with your students about what you’re learning. They’ll respect your honesty and join you in the process.

    Preparing students for what’s next

    AI is not going away. Whether we’re ready or not, it’s going to shape how our students live and work. That gives us a responsibility not just to keep pace with technology but to prepare young people for what’s ahead. The latest futures forecast reminds us that imagining possibilities is just as important as responding to immediate shifts.

    We need to understand both how AI is already reshaping education delivery and how new waves of change will remain on the horizon as tools grow more sophisticated and widespread.

    I want my students to leave my classroom with the ability to question, create, and collaborate using AI. I want them to see it not as a shortcut but as a tool for thinking more deeply and expressing themselves more fully. And I want them to watch me modeling those same habits: curiosity, caution, creativity, and ethical decision-making. Because if we don’t show them what responsible use looks like, who will?

    The future of education won’t be defined by whether we allow AI into our classrooms. It will be defined by how we teach with it, how we teach about it, and how we prepare our students to thrive in a world where it’s everywhere.

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  • Teaching math the way the brain learns changes everything

    Teaching math the way the brain learns changes everything

    Key points:

    Far too many students enter math class expecting to fail. For them, math isn’t just a subject–it’s a source of anxiety that chips away at their confidence and makes them question their abilities. A growing conversation around math phobia is bringing this crisis into focus. A recent article, for example, unpacked the damage caused by the belief that “I’m just not a math person” and argued that traditional math instruction often leaves even bright, capable students feeling defeated.

    When a single subject holds such sway over not just academic outcomes but a student’s sense of self and future potential, we can’t afford to treat this as business as usual. It’s not enough to explore why this is happening. We need to focus on how to fix it. And I believe the answer lies in rethinking how we teach math, aligning instruction with the way the brain actually learns.

    Context first, then content

    A key shortcoming of traditional math curriculum–and a major contributor to students’ fear of math–is the lack of meaningful context. Our brains rely on context to make sense of new information, yet math is often taught in isolation from how we naturally learn. The fix isn’t simply throwing in more “real-world” examples. What students truly need is context, and visual examples are one of the best ways to get there. When math concepts are presented visually, students can better grasp the structure of a problem and follow the logic behind each step, building deeper understanding and confidence along the way.

    In traditional math instruction, students are often taught a new concept by being shown a procedure and then practicing it repeatedly in hopes that understanding will eventually follow. But this approach is backward. Our brains don’t learn that way, especially when it comes to math. Students need context first. Without existing schemas to draw from, they struggle to make sense of new ideas. Providing context helps them build the mental frameworks necessary for real understanding.

    Why visual-first context matters

    Visual-first context gives students the tools they need to truly understand math. A curriculum built around visual-first exploration allows students to have an interactive experience–poking and prodding at a problem, testing ideas, observing patterns, and discovering solutions. From there, students develop procedures organically, leading to a deeper, more complete understanding. Using visual-first curriculum activates multiple parts of the brain, creating a deeper, lasting understanding. Shifting to a math curriculum that prioritizes introducing new concepts through a visual context makes math more approachable and accessible by aligning with how the brain naturally learns.

    To overcome “math phobia,” we also need to rethink the heavy emphasis on memorization in today’s math instruction. Too often, students can solve problems not because they understand the underlying concepts, but because they’ve memorized a set of steps. This approach limits growth and deeper learning. Memorization of the right answers does not lead to understanding, but understanding can lead to the right answers.

    Take, for example, a third grader learning their times tables. The third grader can memorize the answers to each square on the times table along with its coordinating multipliers, but that doesn’t mean they understand multiplication. If, instead, they grasp how multiplication works–what it means–they can figure out the times tables on their own. The reverse isn’t true. Without conceptual understanding, students are limited to recall, which puts them at a disadvantage when trying to build off previous knowledge.

    Learning from other subjects

    To design a math curriculum that aligns with how the brain naturally learns new information, we can take cues from how other subjects are taught. In English, for example, students don’t start by memorizing grammar rules in isolation–they’re first exposed to those rules within the context of stories. Imagine asking a student to take a grammar quiz before they’ve ever read a sentence–that would seem absurd. Yet in math, we often expect students to master procedures before they’ve had any meaningful exposure to the concepts behind them.

    Most other subjects are built around context. Students gain background knowledge before being expected to apply what they’ve learned. By giving students a story or a visual context for the mind to process–breaking it down and making connections–students can approach problems like a puzzle or game, instead of a dreaded exercise. Math can do the same. By adopting the contextual strategies used in other subjects, math instruction can become more intuitive and engaging, moving beyond the traditional textbook filled with equations.

    Math doesn’t have to be a source of fear–it can be a source of joy, curiosity, and confidence. But only if we design it the way the brain learns: with visuals first, understanding at the center, and every student in mind. By using approaches that provide visual-first context, students can engage with math in a way that mirrors how the brain naturally learns. This shift in learning makes math more approachable and accessible for all learners.

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  • 10 reasons to upgrade to Windows 11 ASAP

    10 reasons to upgrade to Windows 11 ASAP

    K-12 IT leaders are under pressure from all sides–rising cyberattacks, the end of Windows 10 support, and the need for powerful new learning tools.

    The good news: Windows 11 on Lenovo devices delivers more than an upgrade–it’s a smarter, safer foundation for digital learning in the age of AI.

    Delaying the move means greater risk, higher costs, and missed opportunities. With proven ROI, cutting-edge protection, and tools that empower both teachers and students, the case for Windows 11 is clear.

    There are 10 compelling reasons your district should make the move today.

    1. Harness AI-powered educational innovation with Copilot
    Windows 11 integrates Microsoft Copilot AI capabilities that transform teaching
    and learning. Teachers can leverage AI for lesson planning, content creation, and
    administrative tasks, while students benefit from enhanced collaboration tools
    and accessibility features.

    2. Combat the explosive rise in school cyberattacks
    The statistics are alarming: K-12 ransomware attacks increased 92 percent between 2022 and 2023, with human-operated ransomware attacks surging over 200 percent globally, according to the 2024 State of Ransomware in Education.

    3. Combat the explosive rise in school cyberattacks
    Time is critically short. Windows 10 support ended in October 2025, leaving schools running unsupported systems vulnerable to attacks and compliance violations. Starting migration planning immediately ensures adequate time for device inventory, compatibility testing, and smooth district-wide deployment.

    Find 7 more reasons to upgrade to Windows 11 here.

    Laura Ascione
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  • How tutors can support student thinking

    How tutors can support student thinking

    Key points:

    Consider the work of a personal trainer. They can explain and model a workout perfectly, but if the athlete isn’t the one doing the lifting, their muscles won’t grow. The same is true for student learning. If students only copy notes or nod along, their cognitive muscles won’t develop. Cognitive lift is the mental work students do to understand, apply, and explain academic content. It’s not about giving students harder problems or letting them struggle alone. It’s about creating space for them to reason and stretch their thinking.

    Research consistently shows that students learn more when they are actively engaged with the material, rather than passively observe. Learners often forget what they’ve “learned” if they only hear an explanation. That’s why great tutors don’t just explain material clearly–they get students to explain it clearly. 

    Tutoring, with its small group format, is the ideal space to encourage students’ cognitive lift. While direct instruction and clear explanations are essential at the right times in the learning process, tutorials offer a powerful opportunity for students to engage deeply and productively practice with support.

    The unique power of tutorials

    Small-group tutorials create conditions that are harder to foster in a full classroom. Having just a few students, tutors can track individual student thinking and adjust support quickly. Students gain more chances to voice reasoning, test ideas, and build confidence. Tutorials rely on strong relationships, and when students trust their tutor, they’re more willing to take risks, share half-formed thoughts, and learn from mistakes. 

    It’s easier to build space for every student to participate and shine in a tutorial than in a full class. Tutors can pivot when they notice students aren’t actively thinking. They may notice they’re overexplaining and can step back, shifting the cognitive responsibility back to the students. This environment gives each learner the opportunity to thrive through cognitive lift.

    What does cognitive lift look like?

    What does cognitive lift look like in practice? Picture two tutorials where students solve equations like they did in class. In the first, the tutor explains every step, pausing only to ask quick calculations like, “What’s 5 + 3?” The student might answer correctly, but solving isolated computations doesn’t mean they’re engaged with solving the equation.

    Now imagine a second tutorial. The tutor begins with, “Based on what you saw in class, where could we start?” The student tries a strategy, gets stuck, and the tutor follows up: “Why didn’t that work? What else could you try?” The student explains their reasoning, reflects on mistakes, and revises. Here, they do the mental heavy lifting–reaching a solution and building confidence in their ability to reason through challenges.

    The difference is the heart of cognitive lift. When tutors focus on students applying knowledge and explaining thinking, they foster longer-term learning. 

    Small shifts, big impact

    Building cognitive lift doesn’t require a complete overhaul. It comes from small shifts tutors can make in every session. The most powerful is moving from explaining to asking. Instead of “Let me show you,” tutors can try “How might we approach this?” or “What do you notice?” Tutoring using questions over explanations causes students to do more work and learn more.

    Scaffolds–temporary supports that help students access new learning–can support student thinking without taking over. Sentence stems and visuals guide thinking while keeping responsibility with the student. Simple moves like pausing for several seconds after questions (which tutors can count in their heads) and letting students discuss with a partner also create space for reasoning. 

    This can feel uncomfortable for tutors–resisting the urge to “rescue ” students too quickly can be emotionally challenging. But allowing students to wrestle with ideas while still feeling supported is where great learning happens and is the essence of cognitive lift.

    The goal of tutoring

    Tutors aren’t there to make learning easy–they’re there to create opportunities for students to think and build confidence in facing new challenges. Just like a personal trainer doesn’t lift the weights, tutors shouldn’t do the mental work for students. As athletes progress, they add weight and complete harder workouts. Their muscles strengthen as their trainer encourages them to persist through the effort. In the same way, as the academic work becomes more complex, students strengthen their abilities by wrestling with the challenge while tutors coach, encourage, and cheer.

    Success in a tutorial isn’t measured by quick answers, but by the thinking students practice. Cognitive lift builds independence, deepens understanding, and boosts persistence. It’s also a skill tutors develop, and with the right structures, even novices can foster it. Imagine tutorials where every learner has space to reason, take risks, and grow. When we let students do the thinking, we not only strengthen their skills, we show them we believe in their potential.

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  • 3 strategies to boost student reading fluency this school year

    3 strategies to boost student reading fluency this school year

    Key points:

    With the new school year now rolling, teachers and school leaders are likely being hit with a hard truth: Many students are not proficient in reading.

    This, of course, presents challenges for students as they struggle to read new texts and apply what they are learning across all subject areas, as well as for educators who are diligently working to support students’ reading fluency and overall academic progress. 

    Understanding the common challenges students face with reading–and knowing which instructional strategies best support their growth–can help educators more effectively get students to where they need to be this school year.

    Understanding the science of learning

    Many districts across the country have invested in evidence-based curricula grounded in the science of reading to strengthen how foundational skills such as decoding and word recognition are taught. However, for many students, especially those receiving Tier 2 and Tier 3 interventions, this has not been enough to help them develop the automatic word recognition needed to become fluent, confident readers.

    This is why coupling the science of reading with the science of learning is so important when it comes to reading proficiency. Simply stated, the science of learning is how students learn. It identifies the conditions needed for students to build automaticity and fluency in complex skills, and it includes principles such as interleaving, spacing practice, varying tasks, highlighting contrasts, rehearsal, review, and immediate feedback–all of which are essential for helping students consolidate and generalize their reading skills.

    When these principles are intentionally combined with the science of reading’s structured literacy principles, students are able to both acquire new knowledge and retain, retrieve, and apply it fluently in new contexts.

    Implementing instructional best practices

    The three best practices below not only support the use of the science of learning and the science of reading, but they give educators the data and information needed to help set students up for reading success this school year and beyond. 

    Screen all students. It is important to identify the specific strengths and weaknesses of each student as early as possible so that educators can personalize their instruction accordingly.

    Some students, even those in upper elementary and middle school, may still lack foundational skills, such as decoding and automatic word recognition, which in turn negatively impact fluency and comprehension. Using online screeners that focus on decoding skills, as well as automatic word recognition, can help educators more quickly understand each student’s needs so they can efficiently put targeted interventions in place to help.

    Online screening data also helps educators more effectively communicate with parents, as well as with a student’s intervention team, in a succinct and timely way.

    Provide personalized structured, systematic practice. This type of practice has been shown to help close gaps in students’ foundational skills so they can successfully transfer their decoding and automatic word recognition skills to fluency. The use of technology and online programs can optimize the personalization needed for students while providing valuable insights for teachers.

    Of course, when it comes to personalizing practice, technology should always enhance–not replace–the role of the teacher. Technology can help differentiate the questions and lessons students receive, track students’ progress, and engage students in a non-evaluative learning environment. However, the personal attention and direction given by a teacher is always the most essential aid, especially for struggling readers. 

    Monitor progress on oral reading. Practicing reading aloud is important for developing fluency, although it can be very personal and difficult for many struggling learners. Students may get nervous, embarrassed, or lose their confidence. As such, the importance of a teacher’s responsiveness and ongoing connection while monitoring the progress of a student cannot be overstated.

    When teachers establish the conditions for a safe and trusted environment, where errors can occur without judgment, students are much more motivated to engage and read aloud. To encourage this reading, teachers can interleave passages of different lengths and difficulty levels, or revisit the same text over time to provide students with spaced opportunities for practice and retrieval. By providing immediate and constructive feedback, teachers can also help students self-correct and refine their skills in real time.

    Having a measurable impact

    All students can become strong, proficient readers when they are given the right tools, instruction, and support grounded in both the science of learning and the science of reading. For educators, this includes screening effectively, providing structured and personalized practice, and creating environments where students feel comfortable learning and practicing skills and confident reading aloud.

    By implementing these best practices, which take into account both what students need to learn and how they learn best, educators can and will make a measurable difference in students’ reading growth this school year.

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  • Why busy educators need AI with guardrails

    Why busy educators need AI with guardrails

    Key points:

    In the growing conversation around AI in education, speed and efficiency often take center stage, but that focus can tempt busy educators to use what’s fast rather than what’s best. To truly serve teachers–and above all, students–AI must be built with intention and clear constraints that prioritize instructional quality, ensuring efficiency never comes at the expense of what learners need most.

    AI doesn’t inherently understand fairness, instructional nuance, or educational standards. It mirrors its training and guidance, usually as a capable generalist rather than a specialist. Without deliberate design, AI can produce content that’s misaligned or confusing. In education, fairness means an assessment measures only the intended skill and does so comparably for students from different backgrounds, languages, and abilities–without hidden barriers unrelated to what’s being assessed. Effective AI systems in schools need embedded controls to avoid construct‑irrelevant content: elements that distract from what’s actually being measured.

    For example, a math question shouldn’t hinge on dense prose, niche sports knowledge, or culturally-specific idioms unless those are part of the goal; visuals shouldn’t rely on low-contrast colors that are hard to see; audio shouldn’t assume a single accent; and timing shouldn’t penalize students if speed isn’t the construct.

    To improve fairness and accuracy in assessments:

    • Avoid construct-irrelevant content: Ensure test questions focus only on the skills and knowledge being assessed.
    • Use AI tools with built-in fairness controls: Generic AI models may not inherently understand fairness; choose tools designed specifically for educational contexts.
    • Train AI on expert-authored content: AI is only as fair and accurate as the data and expertise it’s trained on. Use models built with input from experienced educators and psychometricians.

    These subtleties matter. General-purpose AI tools, left untuned, often miss them.

    The risk of relying on convenience

    Educators face immense time pressures. It’s tempting to use AI to quickly generate assessments or learning materials. But speed can obscure deeper issues. A question might look fine on the surface but fail to meet cognitive complexity standards or align with curriculum goals. These aren’t always easy problems to spot, but they can impact student learning.

    To choose the right AI tools:

    • Select domain-specific AI over general models: Tools tailored for education are more likely to produce pedagogically-sound and standards-aligned content that empowers students to succeed. In a 2024 University of Pennsylvania study, students using a customized AI tutor scored 127 percent higher on practice problems than those without.
    • Be cautious with out-of-the-box AI: Without expertise, educators may struggle to critique or validate AI-generated content, risking poor-quality assessments.
    • Understand the limitations of general AI: While capable of generating content, general models may lack depth in educational theory and assessment design.

    General AI tools can get you 60 percent of the way there. But that last 40 percent is the part that ensures quality, fairness, and educational value. This requires expertise to get right. That’s where structured, guided AI becomes essential.

    Building AI that thinks like an educator

    Developing AI for education requires close collaboration with psychometricians and subject matter experts to shape how the system behaves. This helps ensure it produces content that’s not just technically correct, but pedagogically sound.

    To ensure quality in AI-generated content:

    • Involve experts in the development process: Psychometricians and educators should review AI outputs to ensure alignment with learning goals and standards.
    • Use manual review cycles: Unlike benchmark-driven models, educational AI requires human evaluation to validate quality and relevance.
    • Focus on cognitive complexity: Design assessments with varied difficulty levels and ensure they measure intended constructs.

    This process is iterative and manual. It’s grounded in real-world educational standards, not just benchmark scores.

    Personalization needs structure

    AI’s ability to personalize learning is promising. But without structure, personalization can lead students off track. AI might guide learners toward content that’s irrelevant or misaligned with their goals. That’s why personalization must be paired with oversight and intentional design.

    To harness personalization responsibly:

    • Let experts set goals and guardrails: Define standards, scope and sequence, and success criteria; AI adapts within those boundaries.
    • Use AI for diagnostics and drafting, not decisions: Have it flag gaps, suggest resources, and generate practice, while educators curate and approve.
    • Preserve curricular coherence: Keep prerequisites, spacing, and transfer in view so learners don’t drift into content that’s engaging but misaligned.
    • Support educator literacy in AI: Professional development is key to helping teachers use AI effectively and responsibly.

    It’s not enough to adapt–the adaptation must be meaningful and educationally coherent.

    AI can accelerate content creation and internal workflows. But speed alone isn’t a virtue. Without scrutiny, fast outputs can compromise quality.

    To maintain efficiency and innovation:

    • Use AI to streamline internal processes: Beyond student-facing tools, AI can help educators and institutions build resources faster and more efficiently.
    • Maintain high standards despite automation: Even as AI accelerates content creation, human oversight is essential to uphold educational quality.

    Responsible use of AI requires processes that ensure every AI-generated item is part of a system designed to uphold educational integrity.

    An effective approach to AI in education is driven by concern–not fear, but responsibility. Educators are doing their best under challenging conditions, and the goal should be building AI tools that support their work.

    When frameworks and safeguards are built-in, what reaches students is more likely to be accurate, fair, and aligned with learning goals.

    In education, trust is foundational. And trust in AI starts with thoughtful design, expert oversight, and a deep respect for the work educators do every day.

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