Month: November 2025

NotebookLM for ESL students learning science using AI

This semester, I was given a grade level I had not taught, The high school science course, included ESL students. It was a challenge to meet the expectations for these students. Tried to use AI but I needed topic specific responses and lessons, that is when I discovered NotebookLM. It is well-suited for teaching science to ESL students by addressing the unique challenges of complex scientific vocabulary and concepts. It provides tools to simplify information, offer alternative explanations, and reinforce learning in multiple formats.
Here are specific examples of how you can use NotebookLM for teaching science to ESL students:

  1. Simplifying Complex Scientific Texts
    Science textbooks and articles often use specialized vocabulary and dense sentence structures. NotebookLM can simplify this content: Simplified Explanations: Upload a chapter on “Cell Division” and ask NotebookLM to explain mitosis and meiosis using simple English and analogies. This makes the core biological processes more accessible without watering down the scientific accuracy.
    Vocabulary Builder: Upload a scientific article about climate change and prompt NotebookLM to extract key terms (e.g., “greenhouse gases,” “photosynthesis,” “biodiversity”). It can generate a tailored glossary with definitions and examples relevant to the text, building students’ domain-specific vocabulary.
  2. Creating Differentiated Study Materials
    NotebookLM can generate a variety of study tools based on a single source, catering to different learning preferences: Interactive Study Guides: After students read a text about the “Solar System,” direct them to use NotebookLM to generate a Q&A list or a custom study guide. This encourages independent review and clarification of concepts at their own pace.
    Audio Summaries for Listening Practice: Generate a podcast-style audio summary of a complex topic like “plate tectonics.” ESL students can listen to this summary repeatedly to improve their listening comprehension and reinforce pronunciation of scientific terms.
  3. Fostering Conceptual Understanding through Interaction
    Instead of simply reading, students can interact with the material to deepen their understanding: Analogies and Visual Descriptions: When teaching abstract concepts like “atomic structure” or “electricity,” ask NotebookLM to generate analogies or descriptive language that connects the new scientific idea to something the student already knows.
    Concept Mapping Outlines: Upload lecture notes on “food chains” and prompt NotebookLM to create a structured outline that helps students visualize the flow of energy. This aids in organizing complex information logically.
  4. Supporting Research and Inquiry-Based Learning
    As students work on science projects, NotebookLM can assist with managing the information: Scaffolded Research: For a project on “ecosystems,” students can upload various source documents into their notebook. NotebookLM can help them quickly summarize key findings from each document, making it easier to synthesize information for their final project.
    Clarifying Misconceptions: Encourage students to ask NotebookLM questions about science topics they find confusing. The AI can provide alternative explanations, acting as an always-available, non-judgmental “study buddy” to clarify misconceptions before they become embedded.

By using NotebookLM in these ways, teachers can bridge the language gap and ensure that ESL students have the support needed to fully participate in and succeed in science education. You can begin using these features today at NotebookLM.

I have been successful in engaging the students in the course material. I could shift the learning strategy. Now they feel confident to initiate their own learning by using NotebookLM. Mind you, students will need devices in which they can use the AI program.

Surprising truths about AI in Education

The conversation around artificial intelligence in education often swings between two extremes. On one side, there’s the fear of rampant cheating and academic dishonesty. On the other, there are futuristic promises of fully automated, personalized learning utopias. While these debates capture headlines, they often miss the more immediate and nuanced realities of how AI is actually impacting our schools today.
The most important takeaways for students, teachers, and parents are not found in speculative futures but in the data and guidance available right now. These insights reveal a foundational challenge—the student-teacher adoption gap—and a series of cascading consequences related to equity, pedagogy, and the very social fabric of our schools. This article will reveal four impactful realities from recent research that everyone involved in education should understand to navigate the changes ahead.

Takeaway 1: Students Are Living in an AI World. Most Teachers Are Just Visiting.

There is a massive adoption gap between students and instructors when it comes to generative AI. According to a 2023 national survey by Tyton Partners, 27% of students are regular users of generative AI tools, compared to only 9% of instructors.

The disparity is even starker when looking at initial exposure. The same survey found that while nearly half of all students have tried AI writing tools at least once, an astonishing 71% of instructors have never tried AI tools at all. This gap is arguably the single most critical issue in educational AI today, as it creates a dynamic where the primary users of a transformative technology are being guided by educators who, for the most part, are unfamiliar with how it works, its limitations, and its potential. This makes it incredibly challenging for teachers to set effective policies or teach students how to use these powerful tools responsibly. This lack of instructor familiarity isn’t just a logistical problem; it creates a critical blind spot for emerging risks, including the technology’s hidden biases.

Takeaway 2: AI’s Hidden Bias Can Unjustly Penalize Non-Native English Speakers

Algorithmic bias is one of the most significant challenges in deploying AI tools, and its effects in the classroom can be devastating. Research has uncovered a significant bias in GPT detectors against individuals who are non-native English speakers. One study revealed a shocking finding: over half of writing samples from non-native English speakers were misclassified as being AI-generated. In contrast, the accuracy for native English speakers was nearly perfect.

The underlying reason for this bias is that AI detection tools are often programmed to identify more “literary and complex” language as human. Consequently, writers who use simpler, more direct sentence structures—a common and natural characteristic of someone writing in a second language—are at a high risk of being flagged. The consequences are severe, as students could be falsely accused of cheating, potentially undermining their academic careers and causing significant psychological distress. This danger is amplified when 71% of instructors have never used the tools themselves and may not be equipped to question the output of a flawed AI detector.

Takeaway 3: The Paradox of AI and Human Connection

One of the most promising ideas about AI in education is its potential to increase human interaction. By automating and streamlining administrative tasks like grading, scheduling, and record-keeping, AI could theoretically free up teachers to spend more quality, hands-on time building relationships with their students. This increased capacity for relationship-building is critical, as stronger teacher-student connections have been proven to result in better grades and higher college enrollment rates.

However, the reality on the ground presents a paradox. A recent survey revealed an “all-time low” of only 22% of students who believe their teachers try to understand their lives outside of school. While the potential for AI to foster deeper human connection exists, it is not an automatic outcome. If teachers are not using AI tools to begin with—as the adoption gap clearly shows—they cannot realize the time-saving benefits required to focus more on students. Without an intentional effort from schools to leverage this newfound time for relationship-building, the technology alone will not bridge the growing gap between students and teachers.

Takeaway 4: The Most Important Skill Is Learning With AI, Not From AI

To use AI effectively and ethically, students and educators must adopt a critical mindset shift. The goal is not simply to get answers from AI, but to develop the skills to learn with AI as a partner in the process. This approach treats AI as a tool to augment human intelligence, not replace it.

See AI-generated content as a starting point, not a final product. Use it to spark your thinking, but be sure to add in your own ideas, insights, and final touches.

This mindset is supported by several key principles for responsible AI use:

  • Use AI as a support, not a substitute: Stay actively engaged in the learning process by asking questions and seeking help from teachers and other sources.
  • Verify for bias and accuracy: Always use critical thinking skills to question what AI generates and cross-reference it with other reliable sources.
  • Use your judgment: Remember that AI can produce wrong, biased, or outdated information. Your own knowledge and judgment are essential.
  • Be transparent: If you use an AI tool, credit it appropriately and disclose its contribution to your work. Never pass off AI-generated content as your own.

Adopting this collaborative approach ensures that students are not just learning to prompt a machine but are developing the critical thinking and ethical reasoning skills necessary to thrive in an increasingly AI-driven world.

The Intentional Future of Education

Successfully integrating AI into our schools is not about simply adopting the latest tool. It requires a thoughtful and deliberate approach to navigating complex challenges, including vast user gaps, dangerous hidden biases, and evolving social dynamics in the classroom. The true potential of AI will only be realized if we move beyond the hype and address these realities head-on.

This requires a commitment to fairness, critical thinking, and human-centered learning. As AI becomes more embedded in our schools, we are left with a critical question: How can we intentionally design policies to harness its power while actively protecting our students’ equity and fostering genuine human connection?

Stan's Academy