Educators have been told that today’s students are “digital natives.” The assumption is that because students grew up with smartphones, social media, and constant connectivity, they intuitively know how to use technology effectively.
In practice, many educators know this assumption is flawed.
Students may be highly proficient at consuming digital media, but many struggle with using technology for productivity, learning, and critical thinking. The rise of generative AI has only amplified this challenge. When students encounter AI tools, the temptation is often to ask for answers rather than use them to deepen understanding.
This moment calls for a shift in how educators think about technology in teaching and learning. Digital literacy cannot be outsourced to a single course, a technology office, or assumed as prior knowledge. Every faculty member plays a role in developing students’ ability to use digital tools thoughtfully and responsibly for learning.
One tool that offers meaningful opportunities in this space is Google’s NotebookLM.
Universal Design for Learning (UDL) emphasizes providing multiple means of representation, engagement, and expression so that students can access and demonstrate learning in ways that work best for them.
UDL has long been recognized as a powerful framework for inclusive teaching, but it has also historically been difficult to implement at scale. Creating multiple formats of content for every concept, lesson, or module requires time and resources that many educators simply do not have.
This is where AI tools like NotebookLM can help.
Instead of requiring educators to build multiple versions of learning materials from scratch, NotebookLM can transform existing course materials into different formats quickly. Instructors can upload readings, lecture notes, or source materials and generate alternative representations of that content in minutes.
For example, faculty can use NotebookLM to create:
The value is not simply novelty. It is flexibility. When students encounter information in multiple formats, they have more entry points to understanding complex material.
This is especially powerful in courses where reading-heavy content can become a barrier for some learners. Providing a short podcast overview or visual concept map can help students engage with material before diving into deeper analysis.
AI does not replace good instructional design, but it can dramatically reduce the effort required to bring UDL principles into everyday teaching.
Equally important is helping students understand how to use tools like NotebookLM for their own learning process, not just as shortcuts.
When used thoughtfully, NotebookLM can function as a study companion rather than an answer generator.
Students can upload readings and ask for concept explanations in simpler language, request practice questions based on course material, generate summaries before and after reading to check understanding, ask for comparisons between concepts, or turn notes into audio summaries for review while commuting or exercising.
This approach shifts AI from a tool that bypasses learning to one that supports metacognition and self-advocacy.
Students begin asking questions like:
What parts of this concept am I still struggling with? Can this idea be explained another way? How does this connect to what we learned last week?
Those are the kinds of habits that build durable learning.
Many faculty hesitate to integrate AI tools into teaching because they assume students already understand them better.
In reality, the opposite is often true.
Students may know how to navigate apps quickly, but they frequently lack experience with evaluating AI outputs critically, structuring prompts for meaningful results, using digital tools for analysis and synthesis, and integrating technology into productive learning workflows.
In other words, they are often confident consumers of technology but inexperienced producers of knowledge with it.
Faculty expertise remains essential. Educators bring the disciplinary knowledge and learning design experience that help students use technology purposefully.
Teaching students how to use AI responsibly may become just as important as teaching them what to learn.
My wife, Jenifer Winn, MEd, a learning specialist, recently described a simple but powerful use of NotebookLM.
Before beginning a unit, she uploads her unit plans and instructional materials into the system and asks it to generate a visual infographic that shows students the pathway of learning for the unit.
The infographic illustrates key concepts students will encounter, how each lesson connects to the next, what skills they will build along the way, and where assessments fit into the learning journey.
For many students, especially those who benefit from visual structure, this big-picture roadmap transforms the learning experience.
Instead of feeling like each lesson is disconnected, students can see where they are going and why each step matters.
It also supports self-advocacy. When students understand the structure of a unit, they can better recognize where they may need additional help or practice.
The conversation around AI in education often centers on policy, detection tools, or academic integrity.
Those conversations are important, but they miss a larger opportunity.
AI tools like NotebookLM present a chance to reframe digital literacy as an instructional responsibility shared across disciplines.
History professors, biology instructors, elementary teachers, and writing faculty all play a role in helping students learn how to ask better questions, explore ideas with AI tools, synthesize information across sources, and reflect on their own understanding.
Technology will continue to evolve. What matters most is whether students leave our classrooms knowing how to think with tools, not simply how to use them.
NotebookLM is one example of how educators can begin building that capacity today.
Originally posted by Matthew Winn on LinkedIn. Be sure to follow him there to catch all his great industry insights.
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