Category: eSchool Media

  • Solving our literacy crisis starts in the lecture hall

    Solving our literacy crisis starts in the lecture hall

    Key points:

    The recent NAEP scores have confirmed a sobering truth: Our schools remain in the grips of a literacy crisis. Across the country, too many children are struggling to read, and too many teachers are struggling to help them. But why? And how do we fix it?

    There are decades of research involving thousands of students and educators to support a structured literacy approach to teaching literacy. Teacher preparation programs and school districts across the nation have been slow to fully embrace this research base, known as the science of reading. Since 2017, consistent media attention focused on the literacy crisis has created a groundswell of support for learning about the science of reading. Despite this groundswell, too many educators are still entering classrooms without the skills and knowledge they need to teach reading.

    While there is steady progress in teacher preparation programs to move toward the science of reading-aligned practices, the National Council on Teacher Quality’s latest report on the status of teacher preparation programs for teaching reading (2023) still shows that only 28 percent of programs adequately address all five components of reading instruction. Furthermore, according to the report, up to 40 percent of programs still teach multiple practices that run counter to reading research and ultimately impede student learning, such as running records, guided reading, leveled texts, the three cueing systems, etc. This data shows that there is still much work to be done to support the education of the teacher educators responsible for training pre-service teachers.

    The disconnect between theory and practice

    When it comes to literacy instruction, this problem is especially glaring. Teachers spend years learning about teaching methods, reading theories, and child development. They’re often trained in methods that emphasize comprehension and context-based guessing. However, these methods aren’t enough to help students develop the core skills they need to become proficient readers. Phonics–teaching students how to decode words–is a critical part of reading instruction, but it’s often left out of traditional teacher prep programs.

    One primary reason this disconnect happens is that many teacher prep programs still rely on outdated methods. These approaches prioritize reading comprehension strategies that focus on meaning and context, but they don’t teach the foundational skills, like phonics, essential for developing fluent readers.

    Another reason is that teacher prep programs often lag when it comes to incorporating new research on reading. While the science of reading–a body of evidence built from decades of research and studies involving thousands of students and educators about how humans learn to read and the instructional practices that support learning to read–has been gaining deserved traction, it’s not always reflected in the teacher preparation programs many educators go through. As a result, teachers enter classrooms without the knowledge, skills, and up-to-date methods they need to teach reading effectively.

    A way forward: Structured literacy and continuous professional development

    For real progress, education systems must prioritize structured literacy, a research-backed approach to teaching reading that includes explicit, systematic instruction in phonics, decoding, fluency, and comprehension. This method is effective because it provides a clear, step-by-step process that teachers can follow consistently, ensuring that every single student gets the support they need to succeed.

    But simply teaching teachers about structured literacy is not enough. They also need the tools to implement these methods in their classrooms. The goal should be to create training programs that offer both the theoretical knowledge and the hands-on experience teachers need to make a lasting difference. Teachers should graduate from their prep programs not just with a degree but with a practical, actionable plan for teaching reading.

    And just as important, we can’t forget that teacher development doesn’t end once a teacher leaves their prep program. Just like doctors, teachers need to continue learning and growing throughout their careers. Ongoing professional development is critical to helping teachers stay current with the latest research and best practices in literacy instruction. Whether through in-person workshops, online courses, or coaching, teachers should have consistent, high-quality opportunities to grow and sharpen their skills.

    What do teacher educators need?

    In 2020, the American Federation of Teachers published an update to its seminal publication, Teaching Reading is Rocket Science. First published in 2000, this updated edition is a collaboration between the AFT and the Center on Development and Learning. Although some progress has been made over the past 20 years in teaching reading effectively, there are still too many students who have not become proficient readers.

    This report outlines in very specific ways what pre-service and in-service teachers need to know to teach reading effectively across four broad categories:

    1. Knowing the basics of reading psychology and development
    2. Understanding language structure for word recognition and language comprehension
    3. Applying best practices (based on validated research) in all components of reading
    4. Using validated, reliable, efficient assessments to inform classroom teaching

    There should be a fifth category that is directly related to each of the four areas listed above: the knowledge of how to address the specific oral language needs of multilingual learners and speakers of language varieties. Structured, spoken language practice is at the heart of addressing these needs.

    Moving forward: Reimagining teacher training

    Ultimately, fixing the literacy crisis means changing the way we think about teacher preparation and ongoing professional development. We need to create programs that not only teach the theory of reading instruction but also provide teachers with the practical skills they need to apply that knowledge effectively in the classroom. It’s not enough to just teach teachers about phonics and reading theory; they need to know how to teach it, too.

    Literacy instruction must be at the heart of every teacher’s training–whether they teach kindergarten or high school–and ongoing professional development should ensure that teachers have the support they need to continuously improve.

    It’s a big task, but with the right tools, knowledge, and support, we can bridge the gap between theory and practice and finally begin to solve a literacy crisis that has stubbornly endured for far too long.

    Latest posts by eSchool Media Contributors (see all)

    Source link

  • Why agentic AI matters now more than ever

    Why agentic AI matters now more than ever

    Key points:

    For years now, the promise of AI in education has centered around efficiency–grading faster, recommending better content, or predicting where a student might struggle.

    But at a moment when learners face disconnection, systems are strained, and expectations for personalization are growing, task automation feels…insufficient.

    What if we started thinking less about what AI can do and more about how it can relate?

    That’s where agentic AI comes in. These systems don’t just answer questions. They recognize emotion, learn from context, and respond in ways that feel more thoughtful than transactional. Less machine, more mentor.

    So, what’s the problem with what we have now?

    It’s not that existing AI tools are bad. They’re just incomplete.

    Here’s where traditional AI systems tend to fall short:

    • NLP fine-tuning
       Improves the form of communication but doesn’t understand intent or depth.
    • Feedback loops
       Built to correct errors, not guide growth.
    • Static knowledge bases
       Easy to search but often outdated or contextually off.
    • Ethics and accessibility policies
       Written down but rarely embedded in daily workflows.
    • Multilingual expansion
       Translates words, not nuance or meaning across cultures.

    These systems might help learners stay afloat. They don’t help them go deeper.

    What would a more intelligent system look like?

    It wouldn’t just deliver facts or correct mistakes. A truly intelligent learning system would:

    • Understand when a student is confused or disengaged
    • Ask guiding questions instead of giving quick answers
    • Retrieve current, relevant knowledge instead of relying on a static script
    • Honor a learner’s pace, background, and context
    • Operate with ethical boundaries and accessibility in mind–not as an add-on, but as a foundation

    In short, it would feel less like a tool and more like a companion. That may sound idealistic, but maybe idealism is what we need.

    The tools that might get us there

    There’s no shortage of frameworks being built right now–some for developers, others for educators and designers. They’re not perfect. But they’re good places to start.

    Framework Type Use
    LangChain Code Modular agent workflows, RAG pipelines
    Auto-GPT Code Task execution with memory and recursion
    CrewAI Code Multi-agent orchestration
    Spade Code Agent messaging and task scheduling
    Zapier + OpenAI No-code Automated workflows with language models
    Flowise AI No-code Visual builder for agent chains
    Power Automate AI Low-code AI in business process automation
    Bubble + OpenAI No-code Build custom web apps with LLMs

    These tools are modular, experimental, and still evolving. But they open a door to building systems that learn and adjust–without needing a PhD in AI to use them.

    A better system starts with a better architecture

    Here’s one way to think about an intelligent system’s structure:

    Learning experience layer

    • Where students interact, ask questions, get feedback
    • Ideally supports multilingual input, emotional cues, and accessible design

    Agentic AI core

    • The “thinking” layer that plans, remembers, retrieves, and reasons
    • Coordinates multiple agents (e.g., retrieval, planning, feedback, sentiment)

    Enterprise systems layer

    • Connects with existing infrastructure: SIS, LMS, content repositories, analytics systems

    This isn’t futuristic. It’s already possible to prototype parts of this model with today’s tools, especially in contained or pilot environments.

    So, what would it actually do for people?

    For students:

    • Offer guidance in moments of uncertainty
    • Help pace learning, not just accelerate it
    • Present relevant content, not just more content

    For teachers:

    • Offer insight into where learners are emotionally and cognitively
    • Surface patterns or blind spots without extra grading load

    For administrators:

    • Enable guardrails around AI behavior
    • Support personalization at scale without losing oversight

    None of this replaces people. It just gives them better support systems.

    Final thoughts: Less control panel, more compass

    There’s something timely about rethinking what we mean by intelligence in our learning systems.

    It’s not just about logic or retrieval speed. It’s about how systems make learners feel–and whether those systems help learners grow, question, and persist.

    Agentic AI is one way to design with those goals in mind. It’s not the only way. But it’s a start.

    And right now, a thoughtful start might be exactly what we need.

    Latest posts by eSchool Media Contributors (see all)

    Source link

  • Empowering neurodiverse learners with AI-driven solutions

    Empowering neurodiverse learners with AI-driven solutions

    Key points:

    A traditional classroom is like a symphony, where every student is handed the same sheet music and expected to play in perfect unison. But neurodiverse learners are not able to hear the same rhythm–or even the same notes. For them, learning can feel like trying to play an instrument that was never built for them. This is where AI-powered educational tools step in, not as a replacement for the teacher, but as a skilled accompanist, tuning into each learner’s individual tempo and helping them find their own melody.

    At its best, education should recognize and support the unique ways students absorb, process, and respond to information. For neurodiverse students–those with ADHD, dyslexia, autism spectrum disorder (ASD), and other learning differences–this need is especially acute. Traditional approaches often fail to take care of their varied needs, leading to frustration, disengagement, and lost potential. But with advances in AI, we have the opportunity to reshape learning environments into inclusive spaces where all students can thrive.

    Crafting personalized learning paths

    AI’s strength lies in pattern recognition and personalization at scale. In education, this means AI can adapt content and delivery in real time based on how a student is interacting with a lesson. For neurodiverse learners who may need more repetition, multi-sensory engagement, or pacing adjustments, this adaptability is a game changer.

    For example, a child with ADHD may benefit from shorter, interactive modules that reward progress quickly, while a learner with dyslexia might receive visual and audio cues alongside text to reinforce comprehension. AI can dynamically adjust these elements based on observed learning patterns, making the experience feel intuitive rather than corrective.

    This level of personalization is difficult to achieve in traditional classrooms, where one teacher may be responsible for 20 or more students with diverse needs. AI doesn’t replace that teacher; it augments their ability to reach each student more effectively.

    Recent research supports this approach–a 2025 systematic review published in the EPRA International Journal of Multidisciplinary Research found that AI-powered adaptive learning systems significantly enhance accessibility and social-emotional development for students with conditions like autism, ADHD, and dyslexia.

    Equipping educators with real-time insights

    One of the most significant benefits of AI tools for neurodiverse learners is the data they generate–not just for students, but for educators. These systems can provide real-time dashboards indicating which students are struggling, where they’re excelling, and how their engagement levels fluctuate over time. For a teacher managing multiple neurodiverse learners, these insights are crucial. Rather than relying on periodic assessments or observations, educators can intervene early, adjusting lesson plans, offering additional resources, or simply recognizing when a student needs a break.

    Imagine a teacher noticing that a student with ASD consistently disengages during word problems but thrives in visual storytelling tasks. AI can surface these patterns quickly and suggest alternatives that align with the student’s strengths, enabling faster, more informed decisions that support learning continuity.

    Success stories from the classroom

    Across the U.S., school districts are beginning to see the tangible benefits of AI-powered tools for neurodiverse learners. For instance, Humble Independent School District in Texas adopted an AI-driven tool called Ucnlearn to manage its expanding dyslexia intervention programs. The platform streamlines progress monitoring and generates detailed reports using AI, helping interventionists provide timely, personalized support to students. Since its rollout, educators have been able to handle growing caseloads more efficiently, with improved tracking of student outcomes.

    Meanwhile, Houston Independent School District partnered with an AI company to develop reading passages tailored to individual student levels and classroom goals. These passages are algorithmically aligned to Texas curriculum standards, offering engaging and relevant reading material to students, including those with dyslexia and other learning differences, at just the right level of challenge.

    The future of neurodiverse education

    The promise of AI in education goes beyond improved test scores or sleek digital interfaces, it’s about advancing equity. True inclusion means providing every student with tools that align with how they best learn. This could be gamified lessons that minimize cognitive overload, voice-assisted content to reduce reading anxiety, or real-time emotional feedback to help manage frustration. Looking ahead, AI-driven platforms could even support early identification of undiagnosed learning differences by detecting subtle patterns in student interactions, offering a new frontier for timely and personalized intervention.

    Still, AI is not a silver bullet. Its impact depends on thoughtful integration into curricula, alignment with proven pedagogical goals, and ongoing evaluation of its effectiveness. To be truly inclusive, these tools must be co-designed with input from both neurodiverse learners and the educators who work with them. The score is not yet finished; we are still composing. Technology’s real legacy in education will not be in algorithms or interfaces, but in the meaningful opportunities it creates for every student to thrive.

    Latest posts by eSchool Media Contributors (see all)

    Source link

  • What I learned building an AI tool for my own kids (and millions more worldwide)

    What I learned building an AI tool for my own kids (and millions more worldwide)

    Stay up-to-date with the
    INNOVATIONS
    in K-12 Education Newsletter

    Name