AI-Powered Learning
Transforming Education Through Universal Design and Human+AI Co-creation
Welcome to an interactive workshop designed for K–12 educators who are passionate about creating inclusive, engaging learning environments. Today, we'll explore how artificial intelligence can serve as a powerful ally in implementing Universal Design for Learning (UDL) principles, ensuring every student has access to meaningful educational experiences.
Meet Your Facilitator: Jen Tuten
As Director of Curriculum and Instruction, I have the privilege of working across multiple dimensions of educational excellence. My role encompasses overseeing comprehensive programs that serve all learners—from intervention and enrichment initiatives to academic competitions and emotional counseling services. I'm deeply invested in teacher development, providing mentorship in critical areas including leadership cultivation, curriculum design innovation, student engagement strategies, and differentiation techniques.
Today's workshop represents another passion of mine: empowering educators with practical, innovative tools that make a real difference in students' lives. I'm excited to guide you through an interactive exploration of AI applications that align with UDL principles.
Let's get to know each other before we dive in!
Learning Objectives
This professional development session is designed with clear, measurable outcomes that will immediately impact your instructional practice. By the end of our time together, you'll walk away with concrete skills and resources that address real classroom challenges.
Identify Learning Barriers
Critically examine your current instructional practices to recognize obstacles that may prevent students from accessing content, expressing understanding, or maintaining engagement in learning activities.
Understand UDL Framework
Develop a comprehensive understanding of how Universal Design for Learning principles inform curricular design, creating flexible pathways that accommodate learner variability from the start.
Apply AI Tools
Access and confidently apply at least three artificial intelligence tools specifically selected to support neurodiverse student populations and remove barriers to learning.
Create UDL Resources
Design and develop a practical classroom resource using AI that authentically reflects UDL-aligned design principles, ready for immediate implementation with your students.
Setting the Stage for Inclusive Innovation
Before we dive into specific tools and strategies, it's essential to ground our work in a shared understanding of why this matters. Education has always been about meeting students where they are and helping them reach their potential. However, traditional approaches often inadvertently create barriers that prevent some learners from demonstrating their true capabilities.
The intersection of Universal Design for Learning and artificial intelligence represents a paradigm shift in how we think about accessibility and differentiation. Rather than retrofitting accommodations after the fact, we can now design learning experiences that are inherently flexible and responsive to diverse needs from the very beginning. This proactive approach not only benefits students with identified learning differences but enriches the educational experience for all learners.
Throughout today's session, we'll explore this intersection through hands-on experimentation, collaborative problem-solving, and practical application. Our goal isn't just to introduce you to new technology, but to fundamentally reimagine how we can leverage these tools to create more equitable, engaging, and effective learning environments.
02
What Gets in the Way?
Let's begin with honest reflection. Every educator, regardless of experience or expertise, encounters obstacles that impede student learning. These barriers aren't always obvious, and they're rarely intentional. Sometimes they're embedded in our curriculum materials, sometimes in our instructional routines, and sometimes in the physical or digital spaces where learning occurs.
Take a moment to think about your own classroom context. Consider the students who struggle to engage with content despite your best efforts. Think about those who seem capable but can't quite demonstrate their understanding in traditional formats. Reflect on moments when you've wished you could provide more individualized support but felt constrained by time or resources.

Reflective Practice
Jot down 2-3 common challenges to learning in your classroom. Be specific: rather than "students don't pay attention," consider what specifically prevents engagement. Is it the length of texts? The pace of instruction? The mode of presentation? The format required for responses?
Share Your Thoughts on Our Collaborative Padlet
This exercise isn't about cataloging failures—it's about building awareness. Only by identifying barriers can we systematically address them. And as we'll discover throughout this workshop, many of these obstacles can be significantly reduced or even eliminated through thoughtful application of UDL principles and AI tools.
When a flower doesn't bloom, you fix the environment in which it grows…not the flower.
– Alexander Den Heijer
This profound insight captures the essence of Universal Design for Learning. Too often in education, we've focused on "fixing" students—providing remediation, intervention, or accommodation after identifying a deficit. While support services remain important, UDL challenges us to think differently about the fundamental design of learning experiences.
What if, instead of asking "What's wrong with this student?" we asked "What's wrong with this learning environment?" This shift in perspective moves us from a deficit model to a design model. It acknowledges that learner variability is the norm, not the exception, and that our instructional environments should be designed from the outset to accommodate this natural diversity.
When we create flexible, responsive learning ecosystems—providing multiple means of representation, action and expression, and engagement—we remove barriers before they impede learning. We create conditions where all students can flourish, much like a well-tended garden allows every flower to bloom according to its nature. This is the promise of UDL, and as we'll see, AI can be a powerful tool in cultivating these optimal learning environments.
Universal Design for Learning Framework
The Universal Design for Learning framework, developed by CAST (Center for Applied Special Technology), provides a blueprint for creating learning experiences that are accessible and effective for all students. Grounded in neuroscience research about how humans learn, UDL recognizes that there is no single way to learn that works for everyone.
At its core, UDL is organized around three primary principles, each addressing a critical aspect of the learning process. These principles aren't separate tracks but interconnected dimensions that work together to create comprehensive learning experiences. Understanding these principles is essential for leveraging AI tools effectively in your classroom.
Multiple Means of Representation
The "what" of learning. Students differ in how they perceive and comprehend information. UDL addresses this by providing content in multiple formats—text, audio, video, interactive simulations, and more. This ensures that all learners can access information regardless of sensory, linguistic, or learning differences.
Multiple Means of Action & Expression
The "how" of learning. Students differ in how they can navigate learning environments and express what they know. UDL provides varied ways for students to demonstrate understanding—through writing, speaking, creating, performing, or building—recognizing that the mode of expression shouldn't limit students' ability to show mastery.
Multiple Means of Engagement
The "why" of learning. Students differ in what motivates and engages them. UDL addresses this by providing options for recruiting interest, sustaining effort, and self-regulation. By offering choices and connecting to students' interests and goals, we help all learners become purposeful, motivated participants.
This visual representation of the UDL framework shows how the three principles interconnect to create expert learners who are purposeful and motivated, resourceful and knowledgeable, and strategic and goal-directed. Notice how each principle branches into specific guidelines that provide concrete strategies for implementation.
The framework isn't prescriptive—it doesn't tell you exactly what to do in every situation. Instead, it provides a flexible structure for thinking about instructional design. This flexibility is crucial because contexts, content, and learners vary widely. The guidelines help you make informed decisions about how to reduce barriers and optimize learning opportunities for your specific students and situation.
As we explore AI tools throughout this workshop, we'll continually reference these principles. Each tool we examine serves one or more of these UDL goals, helping you provide the multiple means of representation, action/expression, and engagement that all learners need to succeed.
Table Talk: Applying UDL to Real Scenarios
Theory becomes meaningful when we apply it to real situations. In this collaborative activity, you'll work with colleagues to analyze authentic student scenarios and identify how UDL principles can address specific learning barriers. This exercise will help you develop the analytical skills needed to recognize UDL opportunities in your own classroom.
01
Review Student Scenarios
Access the shared Padlet where you'll find several detailed student scenarios. Each describes a learner facing specific challenges in accessing curriculum, expressing understanding, or maintaining engagement.
02
Identify Learning Barriers
For each scenario, work together to pinpoint the precise barrier(s) preventing student success. Move beyond surface observations to understand underlying challenges related to perception, expression, or motivation.
03
Match UDL Principles
Determine which UDL principle (or principles) would most effectively address each barrier. Consider whether the student needs different ways to receive information, express knowledge, or engage with content.
04
Share Your Thinking
Add a comment to the Padlet explaining your group's reasoning. What specific UDL strategies might help this student? How would you implement them in your classroom?
As you work through this activity, you'll notice that many scenarios could be addressed by multiple UDL principles. This overlap is intentional—effective UDL implementation often involves addressing multiple dimensions simultaneously. The goal isn't to find one "right" answer but to develop flexibility in your UDL thinking and build your capacity to recognize opportunities for reducing barriers in diverse situations.
03
AI in Action
Now that we've established a foundation in UDL principles and practiced identifying learning barriers, it's time to explore how artificial intelligence can help us translate these concepts into classroom practice. AI isn't a replacement for thoughtful pedagogy—it's a powerful amplifier of our professional expertise and a tool for implementing UDL at scale.
The question guiding this section is both practical and profound: How can artificial intelligence help remove learning barriers and achieve real results for all students? As we'll discover, AI tools can provide personalized support, generate differentiated materials, offer alternative representations, and free up teacher time for the high-value instructional interactions that only humans can provide.
We'll examine specific AI applications that align with each UDL principle, exploring both the tremendous potential and important limitations of these technologies. Our goal is to develop informed, critical perspectives on AI in education—understanding what it can do well, where it falls short, and how to leverage it ethically and effectively in service of student learning.
AI is just like a person.
It learns from everything on the internet. So, naturally, it's racist, paranoid, and obsessed with cats.
– Stephen Colbert, The Late Show
Colbert's humor contains an important truth: AI systems reflect the data they're trained on, which means they can perpetuate biases, inaccuracies, and problematic patterns present in that data. While funny, this observation raises serious questions for educators about how we use AI responsibly with students.
AI tools are trained on vast amounts of internet content, which includes both the best and worst of human knowledge and expression. These systems can generate content that appears authoritative but contains factual errors, cultural biases, or inappropriate assumptions. This is why human oversight—your professional judgment—remains absolutely essential when using AI in educational contexts.
AI is probably the most important thing humanity has ever worked on.
I think of it as more profound than electricity or fire. But, you know, maybe with slightly fewer marshmallows.
– Sundar Pichai, CEO of Alphabet/Google
From the perspective of technology leaders like Pichai, AI represents a transformational force comparable to humanity's most foundational innovations. This optimistic view emphasizes AI's potential to solve complex problems, accelerate scientific discovery, and democratize access to information and services—including education.
In education specifically, this optimism manifests in visions of truly personalized learning at scale, instant feedback systems, intelligent tutoring that adapts to each student's needs, and teachers freed from administrative burdens to focus on relationship-building and higher-order instruction. While these possibilities are exciting, they must be balanced with realistic understanding of AI's current limitations and potential risks.
With artificial intelligence we are summoning the demon.
You know all those stories where there's the guy with the pentagram and the holy water, and he's like yeah, he's sure he can control the demon? Doesn't work out.
– Elon Musk at the Aeronautics and Astronautics Centennial Symposium, 2014
Musk's dramatic warning represents the cautious perspective on AI development—concern about unintended consequences, loss of human agency, and potential existential risks from increasingly powerful AI systems. While these concerns focus primarily on advanced AI systems beyond what we're using in classrooms, they remind us to approach AI thoughtfully and maintain appropriate skepticism.
For educators, this cautionary perspective translates into important questions: How might AI tools reinforce rather than reduce educational inequities? Could over-reliance on AI diminish critical thinking skills? What happens to student privacy and data? How do we ensure AI serves human values rather than optimizing for metrics that don't align with deep learning?
We're at the cusp of using AI not to replace teachers, but to superpower them.
Every student could have a personalized tutor, and every teacher a personalized teaching assistant.
– Sal Khan, TED Talk, 2023
Khan's vision represents perhaps the most pragmatic and education-focused perspective on AI. Rather than viewing AI as either savior or threat, he frames it as an augmentation tool that amplifies human capabilities. This aligns closely with our UDL goals—using technology to provide personalized support and remove barriers without replacing the essential human elements of teaching.
This perspective acknowledges that teaching involves far more than content delivery. The relationship-building, social-emotional support, motivation, modeling, and responsive decision-making that expert teachers provide cannot be replicated by AI. However, AI can handle many time-consuming tasks—generating differentiated materials, providing practice opportunities, offering immediate feedback—freeing teachers to focus on the irreplaceable human dimensions of education.
Throughout this workshop, we embrace this balanced view: AI as a powerful tool that, when used thoughtfully and ethically, can help us better implement UDL principles and serve all learners effectively.
If we teach today's students as we taught yesterday's, we rob them of tomorrow.
– John Dewey, author of Experience and Education
Dewey's century-old wisdom remains profoundly relevant. Education must evolve to prepare students for a world that looks very different from the one we grew up in. The skills, knowledge, and dispositions that students need for success in the 21st century—and beyond—require us to continually examine and update our instructional approaches.
This doesn't mean abandoning timeless educational principles or chasing every technological trend. Rather, it means thoughtfully integrating powerful new tools like AI in ways that enhance learning while remaining grounded in sound pedagogy. It means preparing students not just to use AI, but to think critically about it, understand its capabilities and limitations, and leverage it ethically and effectively.
The students in your classrooms will graduate into a world where AI is ubiquitous. Our responsibility is to ensure they develop not just technical fluency, but the judgment, creativity, and human capacities that will allow them to thrive alongside these powerful technologies.
AI in Education: A Historical Perspective
To understand where we are with AI in education, it helps to see it as the latest development in a long history of educational technology adoption. Each innovation sparked similar debates about impact, access, and appropriate use. Understanding this history provides perspective and helps us avoid repeating past mistakes while building on proven principles.
1
1642: Calculators
Early mechanical devices sparked debate about whether students would lose computational skills. Sound familiar? We learned that tools can handle procedures while humans focus on problem-solving and conceptual understanding.
2
1960-70s: Spellcheck
Critics worried students wouldn't learn spelling. Research showed that removing the mechanical burden of perfect spelling freed cognitive resources for higher-order aspects of writing like organization, argument, and style.
3
1990s: Dictation Tools
Speech-to-text technology began removing barriers for students with physical disabilities or writing challenges, foreshadowing today's more sophisticated AI accessibility tools and embodying UDL principles.
4
2010s: Various AI Applications
Photo filters, chatbots, anthropomorphic robots, and deepfakes entered mainstream use, establishing AI in daily life and raising questions about authenticity, privacy, and digital literacy that persist today.
5
2022: Generative AI
Large language models like ChatGPT launched publicly, democratizing access to powerful AI and forcing education to grapple with fundamental questions about learning, assessment, and the purpose of schooling itself.
What patterns emerge from this history? Technology adoption in education follows a predictable cycle: initial skepticism and resistance, followed by experimental use by early adopters, gradual mainstream acceptance, and eventual integration into standard practice. The most successful adoptions occur when technology serves clear pedagogical goals rather than being implemented for its own sake.
Notice also that concerns about technology "doing the thinking for students" have been raised repeatedly—yet education hasn't collapsed, and in many cases, technology has enhanced rather than diminished learning when implemented thoughtfully. The key is understanding what aspects of learning are means versus ends, and ensuring technology supports development of essential skills rather than short-circuiting them.
Humans & Learning: Fundamental Principles
Before diving deeper into AI tools, let's ground ourselves in what we know about how humans actually learn. Despite rapid technological change, the fundamental neuroscience of learning remains constant. Understanding these principles helps us evaluate whether AI applications genuinely support learning or simply provide surface-level convenience.
Speaking: Innate and Structurally Supported
Human brains are wired for oral language. Given exposure to speakers, typically developing children acquire spoken language naturally without formal instruction. This reflects millions of years of evolution—our neural architecture is built for speech.
Reading & Writing: Must Learn from Instruction
Unlike speech, literacy is a recent human invention (only about 5,000 years old). Our brains weren't evolved for reading—we must repurpose neural circuits originally designed for other purposes. This is why reading requires explicit, systematic instruction and why literacy challenges are so common.
Working Memory: The Cognitive Juggle
Our working memory—the mental workspace where we actively process information—is extremely limited. Most people can hold only 4-7 chunks of information in working memory at once. This constraint profoundly impacts learning and is why cognitive load matters so much in instructional design.
Widespread Literacy: A Recent Achievement
Mass literacy is only about 300 years old—an evolutionary eyeblink. The expectation that all children become proficient readers and writers is historically unprecedented, which helps explain why it remains challenging and why we need sophisticated instructional approaches like UDL to achieve it.
These neuroscience insights have important implications for AI use in education. Effective AI tools should work with how humans naturally learn rather than against it. For example, AI that reduces cognitive load by handling mechanical tasks frees working memory for higher-order thinking. AI that provides multimodal representations aligns with how our brains process information. AI that offers personalized scaffolding supports the incremental skill development that learning requires.
Conversely, AI applications that don't account for these learning principles may hinder rather than help. Tools that overload working memory, bypass essential skill development, or provide "answers" without supporting understanding may appear helpful in the short term while actually impeding long-term learning.
Toddlers 1… AI 0
Multisensory Integration
Toddlers seamlessly integrate information from multiple senses, building rich mental models. Current AI excels at processing single modalities but struggles with the kind of holistic, embodied understanding that comes naturally to young children.
Embodiment & Movement
Young children learn through physical interaction with their environment. They understand concepts like "heavy" and "soft" through direct sensory experience. AI, existing only in digital space, lacks this embodied understanding that grounds human cognition.
Social Immersion
Human learning is fundamentally social. Toddlers learn language, social norms, and cultural practices through interaction with caregivers and peers. They read facial expressions, respond to emotional cues, and adjust behavior based on social feedback—capacities far beyond current AI.
Plasticity
Young brains are remarkably plastic, forming new neural connections rapidly in response to experience. While AI systems can be retrained, they lack the organic, experience-dependent neural reorganization that characterizes human brain development.
Motivation & Curiosity
Toddlers are intrinsically motivated to explore, experiment, and learn. Their curiosity drives learning without external rewards. AI has no intrinsic motivation or curiosity—it simply executes programmed functions based on training data.
This comparison isn't meant to diminish AI's capabilities—in narrow domains, AI far exceeds human performance. Rather, it highlights that despite impressive advances, AI doesn't "learn" the way humans do. This has crucial implications for education: we can use AI to support human learning, but we cannot outsource learning to AI.
Principles of AI Use in Education
Given everything we've discussed about learning, UDL, and AI capabilities, what principles should guide our use of AI in educational settings? These guidelines help us leverage AI's strengths while avoiding potential pitfalls and ensuring technology serves pedagogical goals.
Evolution of Learning
The foundations of how humans learn doesn't change at the pace of AI. Weather is not the same as climate! While new tools emerge rapidly, core learning principles remain constant. Ground AI use in established understanding of cognitive science, child development, and effective pedagogy. Don't let technology trends drive instruction—let learning goals drive technology selection.
Human Connection
AI is not great at authentic interpersonal communication and analytical thinking. Education will never work without teachers! The relationships, emotional support, motivation, and responsive human judgment that teachers provide cannot be replicated by algorithms. Use AI to handle tasks that free you for the irreplaceable human aspects of teaching.
Vacuum Cleaners
You can always unplug! Touch some grass! Make sure your students do the same! Technology should enhance rather than dominate the learning experience. Maintain balance. Ensure students have ample opportunities for hands-on activities, outdoor learning, social interaction, and screen-free experiences. AI is a tool, not a substitute for rich, multidimensional learning.
These principles create guardrails for thoughtful AI integration. They remind us that while AI offers exciting possibilities, it remains a tool to be wielded with professional judgment. The goal isn't to use AI because it's cutting-edge, but to use it when it genuinely serves learning—when it removes barriers, provides options, personalizes support, or amplifies our capacity to meet diverse student needs effectively.
The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.
– Alvin Toffler
As we transition from theoretical foundations to hands-on exploration of AI tools, Toffler's prescient observation provides the perfect frame. In a rapidly changing world where AI and other technologies continually reshape work, society, and daily life, the most essential skill is adaptability—the capacity to continuously learn, let go of outdated approaches, and embrace new ways of thinking and working.
This applies equally to teachers and students. For educators, it means maintaining curiosity about new tools and approaches while remaining grounded in timeless pedagogical principles. It means being willing to experiment, reflect on results, and adjust practice based on evidence. For students, it means developing not just content knowledge, but the metacognitive skills, growth mindset, and learning strategies that enable lifelong learning.
As we explore specific AI applications in the next section, approach them with this mindset: What can I learn from experimenting with this tool? How might it help my students develop the adaptability and learning capacity they'll need for their futures? How can I model thoughtful technology adoption and critical evaluation for my students?
On a scale of Renaissance child…
How are you doing so far?
Let's pause for a quick check-in. We've covered substantial ground—UDL principles, learning science, historical context, and guiding principles for AI use. That's a lot to process! This is a good moment to assess your current state and recalibrate before we dive into hands-on tool exploration.
Consider: Are you energized and ready to experiment, or feeling overwhelmed by information? Are certain concepts clear while others remain fuzzy? Do you have questions you'd like addressed? Taking stock of your own learning state models the metacognitive awareness we want to cultivate in students.
Remember that learning isn't linear—it's normal to feel moments of confusion or uncertainty as you integrate new concepts. That slight discomfort often signals productive struggle, the kind of challenge that leads to deeper understanding. If you're feeling it, you're growing. And if you're feeling confident, great—channel that energy into the upcoming hands-on exploration!
04
The AI Sandbox
Time to dig in! This section represents the heart of our workshop—hands-on experimentation with AI tools specifically selected to support UDL implementation. You'll have the opportunity to explore multiple platforms, try different approaches, and discover which tools resonate with your teaching context and student needs.
Think of this as a sandbox in the truest sense: a space for playful exploration, experimentation, and discovery without high stakes. You won't break anything, and there are no wrong answers. The goal is to develop familiarity and confidence with these tools so you can later implement them purposefully in your instructional practice.
As you explore, keep these questions in mind: Which UDL principle does this tool support? What specific barrier does it help remove? How might I adapt this for my students and content? What limitations or concerns do I notice? This critical lens will help you move beyond surface-level engagement to develop the judgment needed for effective implementation.
We'll work through this exploration together, starting with tool overviews and demonstrations, then moving to individual and small-group experimentation, and concluding with collaborative sharing of discoveries and insights. Let's begin!
AI Tools for UDL: A Comprehensive Toolkit
Below you'll find a curated collection of AI-powered tools, each selected for its potential to support UDL principles and remove learning barriers. This isn't an exhaustive list—new tools emerge constantly—but rather a strategic selection representing different approaches and use cases. All links are active and accessible for immediate exploration.
Content Creation & Differentiation
  • MagicSchool – Comprehensive suite for creating differentiated materials
  • Newsela – Current events articles at varied Lexile levels
  • Tchr.Tools – Quick generators for various classroom resources
Assessment & Feedback
  • Curipod – Interactive lessons with embedded formative assessment
  • Q-Chat – AI tutor that helps students study
  • Brainly – Peer-to-peer learning with AI moderation
Accessibility & Support
  • Read&Write – Comprehensive literacy support toolbar
  • Grammarly – Writing assistance with accessibility features
  • Otter.ai – Real-time transcription for lectures and discussions
Personalization & Tutoring
  • Khanmigo – AI tutor integrated with Khan Academy content
  • Spark Studio – Personalized practice and adaptive learning
Multimedia & Engagement
  • YourStory – Student storytelling with multimedia
  • Sora or Suno – AI-generated video and audio content
General-Purpose AI Assistants
  • ChatGPT – Versatile conversational AI
  • Claude – Anthropic's AI assistant with strong reasoning
Collaboration & Organization
  • Mural – Visual collaboration workspace
  • Riff – Meeting assistant and summarizer
  • Jungle – AI workspace for project management
Writing & Language Support
  • Quillbot – Paraphrasing and writing enhancement
As you review this toolkit, notice how different tools serve different UDL functions. Some provide multiple means of representation (like text-to-speech or multilevel reading materials). Others support action and expression (like speech-to-text or multimodal creation tools). Still others address engagement through personalization, choice, and relevance. The most powerful implementation often involves combining multiple tools to create comprehensive support systems.
05
Collaborative Sharing
Now that you've had time to explore various AI tools, let's bring our discoveries back together. Collective wisdom is powerful—each person's experimentation and insights enriches everyone's learning. This collaborative sharing helps us build a community of practice where we can continue supporting each other's AI integration efforts.
Return to our shared Padlet where you initially posted learning challenges. Now you'll add a new dimension: recommendations for AI tools that could address those specific barriers. This creates a crowdsourced problem-solving resource that maps real classroom challenges to practical AI solutions.

Sharing Task
Review the student scenarios and learning challenges posted on the Padlet. For at least one scenario, add a comment recommending a specific AI tool that could help address that barrier. Explain briefly how you would use the tool and why you think it aligns with UDL principles for this situation.
As you read others' contributions, you'll discover tools and approaches you might not have explored yourself. Pay attention to creative applications—sometimes the most powerful uses of AI aren't obvious at first glance. Consider bookmarking particularly helpful comments or reaching out to colleagues whose ideas resonate with you. This Padlet becomes a living resource you can reference and add to over time.
Design Time: Create Your UDL Mini-Unit
Theory and exploration are valuable, but the real test of learning is application. In this culminating activity, you'll synthesize everything from today's workshop—UDL principles, AI tools, and design thinking—to create an original instructional resource you can implement immediately in your classroom.
You'll develop a mini-unit (a 2-3 lesson sequence) that incorporates both UDL principles and AI tools to remove barriers and support diverse learners. This isn't about creating something perfect—it's about applying your new knowledge in a concrete, practical way that you can refine through classroom implementation and reflection.
target
Identify Learning Goals
Start with clear learning objectives. What will students know or be able to do? Ground your mini-unit in standards and essential understandings for your content area and grade level.
barrier
Anticipate Barriers
Consider your specific students. What obstacles might prevent access to content, expression of understanding, or sustained engagement? Be specific about the barriers you're designing to address.
framework
Apply UDL Principles
Design with flexibility from the start. How will you provide multiple means of representation, action/expression, and engagement? Make deliberate choices about where options serve learning goals.
robot
Integrate AI Tools
Select AI tools strategically based on the barriers you identified. Use them purposefully to provide support, differentiation, or options that would be difficult to offer without technology.
A planning template is provided to scaffold your design process. It prompts you to articulate learning goals, identify barriers, map UDL principles to instructional decisions, specify AI tools and their purposes, and plan assessment that honors multiple means of expression. Work individually or collaboratively—both approaches have value.
As you work, remember that UDL doesn't mean providing every possible option for every lesson—that would be overwhelming. It means thinking systematically about barriers and providing strategic flexibility where it matters most. Your mini-unit should demonstrate thoughtful integration of AI in service of learning goals, not technology for technology's sake.
Thank You!
Congratulations on completing this intensive professional development experience! You've engaged with complex concepts, experimented with new tools, and created practical resources for your classroom. That represents significant learning and professional growth worth celebrating.
Today, You Are Leaving With:
A UDL-aligned hyperlinked pret-a-porter toolkit
Ready-to-use collection of AI tools mapped to UDL principles, saving you countless hours of research and evaluation.
An organic Padlet designed to meet YOUR authentic needs
Crowdsourced resource connecting real classroom challenges to practical AI solutions, continuously growing through community contributions.
A network of like-minded educators to lean on for support
Professional learning community committed to thoughtful AI integration and UDL implementation—colleagues who understand your challenges and celebrate your successes.
An invitation to join me in the Curiosity Lab
Ongoing support space for continued exploration, troubleshooting, and collaboration as you implement these approaches in your practice.
But perhaps most importantly, you're leaving with a new lens for thinking about technology in education. You understand how to evaluate AI tools through a UDL framework, asking not just "What can this do?" but "How does this remove barriers and support diverse learners?" This critical perspective will serve you well as you navigate the rapidly evolving landscape of educational technology.
Bespoke Support for You and Your Team
Fridays 12:00-12:45
The Curiosity Lab offers personalized, ongoing support as you implement AI and UDL in your classroom. Think of it as office hours specifically for educational technology innovation—a dedicated time and space where you can bring your questions, challenges, and ideas for collaborative problem-solving.
Whether you need help troubleshooting a specific tool, want feedback on a lesson you're designing, or simply want to explore new possibilities with colleagues, the Curiosity Lab provides the supportive structure for continued growth. Come with specific questions or just show up ready to experiment and learn alongside other curious educators.
This isn't another required meeting—it's an optional, open-door resource designed to meet you where you are in your AI integration journey. Drop in once to try it out, or become a regular participant. The lab adapts to your needs and interests, true to UDL principles we've explored today.
Curiosity Lab: Ongoing Support
Educating the mind without educating the heart is no education at all.
– Aristotle
As we conclude, Aristotle's ancient wisdom reminds us what matters most in education. All the technology, all the pedagogical frameworks, all the innovative tools mean nothing if we lose sight of the human heart of teaching and learning—the relationships, empathy, and genuine care that transform education from information transfer into life-changing experience.
AI can help us be more efficient and effective, but it cannot replace the emotional intelligence, ethical judgment, and authentic connection that define great teaching. As you implement these tools, never lose sight of why you became an educator in the first place: to make a difference in young people's lives, to open minds and hearts, to inspire curiosity and passion for learning.
Use AI to amplify your impact, free your time for meaningful interactions, and remove barriers that prevent students from flourishing. But always remember that the most important technology in any classroom is the human teacher who sees, knows, and cares for each student as a whole person, not just a learning outcome to optimize.
Dramatic Exit (Ticket)
Before you go, let's make sure we stay connected! Here's a quick four-step process that serves as both exit ticket and networking opportunity. It takes just a few minutes but creates lasting professional connections.
Find Me on LinkedIn
Visit www.linkedin.com/in/jen-tuten and send a connection request
Explore Favorite Quotes
Check out the educational quotes and resources I've shared on my profile
Leave a Comment
Share your thoughts on one of the quotes—and let me know what resonated from today's workshop!
We're Connected!
Now we can continue supporting each other's professional growth, sharing resources, and celebrating successes
LinkedIn connections might seem small, but they represent commitment to ongoing professional learning and community. By connecting, you're joining a network of innovative educators who can offer support, inspiration, and practical ideas as you implement what you've learned today. I look forward to seeing your reflections and staying connected as your AI integration journey continues!
Let's Stay in Touch
While we've reached the end of our formal workshop time, this is really just the beginning of your AI and UDL implementation journey. I'm committed to supporting you beyond today's session and would love to hear about your experiences, challenges, and successes as you apply these concepts in your classroom.
Whether you have questions about specific tools, want to share a success story, need troubleshooting help, or simply want to continue the conversation, please don't hesitate to reach out. Education is inherently collaborative work, and we're stronger when we support each other's growth and innovation.
JEN TUTEN
Director of Curriculum & Instruction
Marin Primary & Middle School
Email: jtuten@mpms.org
I'm passionate about supporting teachers in creating inclusive, innovative learning environments where all students thrive. Whether you're just beginning to explore AI tools or looking to deepen your implementation, I'm here to help. Reach out anytime!
AI in Education
The following sections provide additional resources, examples, and deeper dives into specific applications of AI for supporting student learning. These materials are designed for reference and further exploration—bookmark them, return to them as you experiment with implementation, and use them to troubleshoot challenges or discover new possibilities.
While the main workshop focused on building foundational understanding of UDL and introducing a toolkit of AI resources, these supplementary sections offer more granular guidance on specific use cases. They demonstrate step-by-step how to leverage AI for common instructional challenges, with concrete prompts, examples, and student-facing applications.
Think of what follows as your go-to reference guide—practical support for those moments when you're planning lessons and wondering "How might AI help with this?" These examples are starting points for your own experimentation and adaptation to your unique context.
Work Smarter Not Harder!
One of AI's greatest gifts to educators is time. Teaching is incredibly demanding work with seemingly infinite tasks competing for limited hours. Generative AI can handle many time-consuming but lower-complexity tasks—creating differentiated materials, generating practice problems, drafting communication—freeing you for the complex, high-value work that requires human expertise: responsive instruction, relationship building, nuanced assessment interpretation, and creative problem-solving.
The examples that follow demonstrate how AI can specifically support the creation of accommodations and modifications that address diverse learning needs. These aren't tasks AI does automatically—they require your professional judgment about what students need and your expertise in refining AI outputs for quality and appropriateness. But AI dramatically accelerates the process, making it feasible to provide robust differentiation that might otherwise be unsustainable given time constraints.
As you review these examples, notice the pattern: clear input from the educator about what's needed, AI generation of draft materials, and implied teacher review and refinement. AI is a partner in this process, not a replacement for professional judgment. Use it to work smarter, not harder, but always maintain your essential role as instructional decision-maker.
Achieving Mastery
Efficient & Effective Student Accommodations
Accommodations are adjustments that remove barriers to learning without changing what students are expected to learn. They level the playing field, allowing students with different needs to access curriculum and demonstrate understanding. Common accommodations include extended time, alternative formats, assistive technology, and strategic scaffolding.
Traditionally, creating robust accommodations has been resource-intensive. Teachers must develop multiple versions of materials, create scaffolds from scratch, and individualize support—all while managing the demands of whole-class instruction. This workload often means accommodations are less comprehensive or less consistently applied than intended, not because teachers don't care, but because there simply aren't enough hours in the day.
AI changes this equation. With thoughtful prompts, AI can rapidly generate differentiated materials, scaffolds, and supports that would take hours to create manually. This doesn't eliminate teacher work—you still need to refine outputs and make professional judgments—but it makes comprehensive accommodation much more feasible. The following examples show specific accommodation strategies and how AI can support their implementation.
The Challenge
Your students need help breaking down their projects into manageable pieces. Many learners struggle with executive function skills like planning, organizing, and sequencing complex tasks. Long-term projects can feel overwhelming, leading to procrastination, anxiety, or incomplete work—not because students lack capability, but because they don't know how to decompose the large assignment into actionable steps.
This barrier affects learning in multiple ways. Students may produce work below their actual knowledge level simply because they couldn't navigate the planning process. They miss opportunities to receive feedback at checkpoints. They experience unnecessary stress that could be prevented with better scaffolding. And teachers may misidentify executive function challenges as lack of effort or understanding.
Accommodation: Chunk Assignments
One effective accommodation for students who struggle with complex task management is to explicitly break assignments into smaller, discrete steps. Rather than giving one large assignment with a distant due date, you provide a sequence of smaller milestones with clear deliverables and timelines. This chunking scaffolds executive function skills while students are still developing them.
AI can generate these breakdowns quickly based on the assignment parameters you provide. Here's a sample prompt and resulting output:

Sample AI Prompt
I am a fifth grade student and I need to create a PowerPoint presentation about my favorite world explorer. Can you break this down into steps for me?
Notice several key features of this prompt: it specifies the student's grade level (ensuring age-appropriate language and expectations), clearly describes the assignment, and explicitly asks for a breakdown into steps. This gives the AI the context needed to generate useful output.
The resulting AI-generated breakdown would include specific, sequential steps like: choose your explorer, research basic information, organize findings into categories, create presentation outline, design slides, practice presenting, etc. Each step is discrete and accomplishable, reducing cognitive overwhelm.
Useful for Parents and Teachers
These AI-generated breakdowns serve multiple audiences. Teachers can use them to create assignment guides or rubrics with clear checkpoints. Parents receive concrete guidance about how to support homework completion without doing the work for their child. And students themselves gain a model for how to approach complex tasks independently.
This demonstrates a key principle of UDL: supports that benefit students with identified needs often benefit everyone. Many students who don't "require" chunked assignments still perform better when provided with this scaffold. By designing with barriers in mind, we create better learning experiences universally.
Student-Friendly Language
Notice how AI can adjust language complexity based on your prompt. When you specify "I am a fifth grade student," the AI automatically calibrates vocabulary, sentence structure, and conceptual complexity appropriately. This linguistic differentiation would typically require significant teacher time to achieve—writing different versions for different reading levels.
You can further refine by requesting even simpler language, specifying English language proficiency levels, or asking for translations. The ability to rapidly generate multiple versions means you can provide truly individualized language support rather than settling for one-size-fits-all materials that may be inaccessible to some learners.
Further Honed for Clarity and Brevity
Initial AI outputs often need refinement. They might be too wordy, include unnecessary details, or lack the precise focus you need for your specific context. This is where your professional judgment becomes crucial—you review, edit, and improve the AI-generated content to ensure it truly serves your students' needs.
You might streamline language, adjust sequencing, add specific resources or reminders relevant to your classroom, or incorporate visuals. Think of AI output as a strong first draft that accelerates your work but doesn't eliminate the need for thoughtful revision. The goal is efficiency, not elimination of teacher expertise.
With practice, you'll get better at prompting AI to generate outputs that need less revision. You'll learn what details to include in prompts, how to specify format preferences, and when to break complex requests into multiple smaller prompts. This prompt engineering skill itself becomes part of your professional toolkit.
The Challenge
Your students struggle, either physically or attentively, to take live notes during a lecture. Some students have fine motor challenges that make writing laborious and slow. Others have attention differences that make it difficult to simultaneously listen, process information, and capture key points in writing. Some are still developing English proficiency and can't keep pace with native speakers.
For these students, note-taking during instruction creates a painful dilemma: focus on writing and miss what's being said, or focus on listening and have no record for later review. Neither option supports learning effectively. They often end up with incomplete, disorganized notes that provide little value for study or review, despite genuine effort.
Accommodation: Provide Class Notes
Providing structured notes that students can annotate during instruction removes the barrier of having to generate notes from scratch while learning new content. These aren't complete notes that eliminate student engagement—they're skeletal frameworks that still require active participation through annotation, highlighting, drawing connections, and adding examples.
AI can generate these note frameworks quickly based on your lesson content. Here's a sample prompt:

Sample AI Prompt
Can you create a page of notes on minerals that my 6th grade students can annotate during a lecture? Please use language that an 11 year old child can easily understand.
The AI would generate structured notes with clear headings, key vocabulary, space for definitions or examples, and perhaps some prompting questions or diagrams to complete. The format provides organization and captures essential content while leaving room for students to personalize through their own annotations and connections.
Students Can Annotate, Doodle, Discuss, or Expand
Well-designed note scaffolds actively engage students rather than passively receiving information. Students might highlight key points in different colors based on categories you provide. They could draw diagrams or visual representations of concepts. They might add examples from their own experience or questions to investigate further.
During instruction, you can direct students' annotation: "In the margin next to where it talks about igneous rocks, sketch what you think that process looks like." This keeps students mentally active and creates personalized study materials that are more meaningful than generic notes would be.
This accommodation demonstrates the UDL principle of providing multiple means of action and expression—students show engagement and understanding through annotation rather than generation of notes from scratch. It also illustrates how removing one barrier (note generation) doesn't eliminate rigor; students still process information deeply, just through different means.
The Challenge
Your students are challenged with planning and organizing their ideas for writing. Many learners struggle with the pre-writing phase—generating ideas, organizing thoughts, and creating coherent structure before drafting. This difficulty often manifests as blank page paralysis, disorganized writing that jumps between ideas, or inability to elaborate on promising initial thoughts.
Writing is complex cognitive work involving multiple processes: generating ideas, organizing them logically, translating thoughts into words, managing mechanics, and monitoring coherence. When students struggle with the organizational dimension, it bottlenecks the entire process, preventing them from demonstrating their full capabilities as writers.
Accommodation: Create Graphic Organizers
Graphic organizers externalize the planning process, providing visual structure that helps students organize thinking before writing. Different organizer types support different writing purposes: webs for brainstorming, sequence charts for narratives, Venn diagrams for comparison, etc. These tools scaffold the challenging organizational work while students focus on generating and developing ideas.
AI can suggest appropriate organizer structures based on your assignment and even generate templates. Here's a sample prompt:

Sample AI Prompt
Can you give me a template for a graphic organizer that my students could use for brainstorming a personal narrative?
The AI would describe an appropriate organizer structure—perhaps a narrative arc diagram with spaces for beginning (setting/characters), conflict, rising action, climax, falling action, and resolution. It might include prompts like "What happened?" "How did you feel?" "What did you learn?"
Use ChatGPT's Outline to Create Visuals
The AI's text-based description of an organizer structure provides the blueprint, but you'll likely want to create an actual visual template students can write on. You might design this in a word processor, drawing program, or even hand-sketch it.
This step—translating AI text output into polished student-facing materials—represents the teacher value-add. You incorporate your knowledge of students' needs, adjust the complexity appropriately, add visual appeal, and ensure the format works for your specific context. AI accelerates the conceptualization phase, but you bring it across the finish line.
Over time, you might build a library of AI-generated then teacher-refined organizer templates for different purposes. This becomes a reusable resource that benefits multiple cohorts of students and can be shared with colleagues, multiplying the efficiency gains.
The Challenge
Your students need a preview of upcoming content in order to build background knowledge and reinforce concepts. Some learners benefit significantly from seeing material before formal instruction—it primes their thinking, builds vocabulary, and creates mental frameworks for organizing new information. Without this preview, they may struggle to keep pace when content is first introduced.
This need for previewing is especially important for English language learners who need time to process academic language, students with processing speed differences who benefit from extended exposure, and those with gaps in prior knowledge who need additional context to connect new learning to existing understanding.
Accommodation: Preview Content
Content previewing provides students advance exposure to concepts, vocabulary, and frameworks before formal instruction begins. This might involve pre-reading materials at appropriate levels, watching introductory videos, exploring websites, or engaging with interactive simulations. The preview doesn't teach the full content but builds the foundation for successful learning during instruction.
AI can help identify appropriate preview resources based on your upcoming content. Here's a sample prompt:

Sample AI Prompt
Can you recommend some websites where I can preview 7th grade pre-algebra concepts?
The AI would generate a list of relevant websites with brief descriptions of what each offers. This saves you substantial research time—rather than spending hours evaluating resources, you get curated suggestions you can quickly review and select from.
Preview All Sites for Quality Before Recommending
Critical caveat: AI-generated resource recommendations require human verification. AI may suggest sites that no longer exist, have changed content, contain inaccuracies, or include inappropriate advertisements or links. Never share resources with students or families without personally reviewing them first.
This verification step is non-negotiable. It's also an opportunity to apply your professional judgment—evaluating whether resources truly match your students' needs and your instructional goals. AI suggests possibilities; you make final selections.
That said, even with the verification requirement, AI-generated resource lists save tremendous time. Instead of starting from scratch with a search engine, you have targeted suggestions to evaluate. This directed search is far more efficient than open-ended browsing.
The Challenge
Your students need support with what to study as well as how to study. Many learners struggle not because they don't study, but because they study ineffectively—re-reading without active processing, focusing on wrong content, or using strategies that create illusions of learning without supporting actual retention and understanding.
Effective studying involves metacognitive skills many students haven't yet developed: identifying key concepts, generating meaningful questions, self-testing, spacing practice, and connecting new information to prior knowledge. Without explicit support in these study strategies, many students invest significant time with minimal learning gains.
Accommodation: Provide Study Guides
Study guides serve dual purposes: they identify the most important content to focus on, and they model effective study strategies. Good study guides don't just list topics but provide structured ways to engage with content—practice questions, vocabulary activities, graphic organizers, self-assessment checkpoints, etc.
AI can generate comprehensive study guides based on your content. Here's a sample prompt:

Sample AI Prompt
I am an 8th grade student and I need to review the 5 most important ideas about force and motion. Can you create a study guide for me?
The AI would identify core concepts (Newton's laws, friction, momentum, etc.), provide clear explanations at appropriate language level, include practice problems or application questions, and potentially suggest study strategies like creating flashcards or teaching concepts to someone else.
Not Only Content, But Also Study Tips!
Effective AI-generated study guides often include metacognitive support—suggestions about how to use the guide effectively, reminders to self-test rather than just re-read, recommendations for spaced practice, or tips for identifying where understanding is weak.
This embedded study strategy instruction is valuable for all students, not just those who "need" accommodations. It helps develop the self-regulated learning skills that correlate with academic success across domains and grade levels.
As with other AI-generated materials, you'll want to review and refine study guides before sharing them. Ensure they align with your instructional emphasis, add examples from class discussions, incorporate specific resources students have access to, and adjust complexity as needed for your learners.
Turn & Talk
Now that we've explored several concrete examples of how AI can support the creation of accommodations, take a moment to process and consolidate your learning. Discussing with a colleague helps clarify your thinking and may spark additional ideas or applications you hadn't considered.

Discussion Prompt
What is one strategy you learned today about using AI to help your students achieve mastery?
Consider: Which example resonated most with your teaching context? What accommodation could you imagine implementing first? What questions or concerns remain?
Engaging Minds
Teaching & Learning with Artificial Intelligence
Beyond accommodations and differentiation, AI offers exciting possibilities for enhancing core instructional activities in ways that deepen engagement and learning. The following sections explore some creative pedagogical applications—ways to leverage AI not just for efficiency but for instructional approaches that might be difficult or impossible without technology support.
These examples demonstrate how AI can facilitate higher-order thinking, support authentic skill development, and create engaging learning experiences aligned with sound pedagogical principles. As you review them, consider how they might adapt to your content area and grade level. Think creatively about variations and extensions that serve your specific instructional goals.
Error Analysis
Error analysis—systematically examining mistakes to understand underlying misconceptions—is one of the most powerful learning strategies available. When students analyze errors (their own or others'), they engage in metacognition, develop problem-solving skills, and build deeper conceptual understanding. Research consistently shows that productive struggle and learning from mistakes leads to more durable learning than simply getting correct answers.
Deepens learning and critical analysis
Students move beyond surface procedures to examine why errors occurred and what they reveal about conceptual understanding.
Enhances problem-solving skills
Diagnosing errors requires systematic thinking, hypothesis generation, and logical reasoning—transferable skills applicable across domains.
Fosters growth mindset and creative thinking
Framing errors as learning opportunities rather than failures cultivates resilience and intellectual risk-taking.
Enhances metacognitive abilities
Thinking about thinking—monitoring comprehension, identifying confusion, and adjusting strategies—is foundational for lifelong learning.
"I have not failed. I have just found 10,000 ways that won't work."
– Thomas Edison
Leveraging AI for Error Analysis Activities
Writing: Ask AI to Generate Text with Specific Errors
Instead of spending time creating flawed writing samples yourself, prompt AI to generate text containing specific error types you want students to identify and correct. For example: "Write a paragraph about photosynthesis that contains three subject-verb agreement errors and two run-on sentences."
Students then become editors, hunting for errors, correcting them, and explaining the rules that apply. This is far more engaging than worksheet exercises and more closely approximates authentic writing tasks. It also allows you to target exactly the skills students need to practice.
This approach works across content areas: AI can generate historical analyses with factual errors, scientific explanations with conceptual mistakes, or persuasive essays with logical fallacies. The key is being specific in your prompts about what types of errors to include.
Math: Error Analysis with AI
Ask AI to generate math problems that have been solved incorrectly, with work shown, and have students perform error analysis—identifying where mistakes occurred, explaining the misconception, and showing correct solutions.
Extension: Have students use their error analysis to prepare a reteaching lesson. This deepens learning by requiring them to understand the concept well enough to explain it to others and anticipate common mistakes.
Multimedia Twist: Have students create their reteaching lesson as a Khan-style video—explaining the concept, demonstrating the correct process, and highlighting where errors commonly occur. This leverages multiple means of action and expression while building valuable communication and digital creation skills.
Sample prompt: "Generate 3 fraction division problems that have been solved incorrectly, showing common mistakes 5th graders make. Include the incorrect work so students can identify where the error occurred."
This instructional approach accomplishes multiple goals simultaneously: it provides targeted practice, develops analytical thinking, builds metacognitive awareness, and creates opportunities for peer teaching—all while leveraging AI to make the activity feasible at scale. You're not just