Personalized learning is a goal in nearly every classroom, but in practice, it often feels out of reach. Teachers juggle diverse student needs, academic levels, language barriers, disabilities and engagement challenges — all in one room.

Pillar 3 of SREB’s Guidance for the Use of AI in the K12 Classroom tackles this challenge head-on, focusing on how AI can support more individualized instruction.

This pillar isn’t about replacing teachers. It’s about giving educators practical tools to better support every student — especially when there’s not enough time, hands or resources to do it all manually.

In this episode of Class-Act Coaching, I sat down with Leslie Eaves, SREB’s director of project-based learning, to explore how AI can make personalized learning more manageable and meaningful.

 

What Is Pillar 3?

Pillar 3 is “Use AI to support personalized learning.” And, as Leslie says in the interview, this one is pretty self-explanatory.  It’s using AI to support personalized learning. However, to see how to do this, we first must look at why personalized learning matters.

Personalization, or differentiation, helps ensure that all of your students are getting the support that they need to develop their skills and knowledge. While this has long been a goal in schools, it can be difficult to create the personalization that your students need when there is one of you and 30 of them…multiplied by however many classes you teach if you don’t have the same students all day.

That is why finding ways to use AI to accomplish this goal can be so beneficial. First, though, let’s talk about what students could benefit from personalized learning. (Hint: The answer is all of them!)

 

Broadening the Definition of Personalization

Often, personalized learning is viewed as something meant only for struggling students. But our discussion emphasized that it should extend to all students — including those who are ahead,  bored or just learn differently.

Every student comes to your class to learn, even if they’d tell you they have no interest in learning. However, each student learns in their own way and at their own pace. They have their own interests and their own styles.

Imagine being able to engage with them in a way that directly appeals to them.

Along with just engaging their interests, though, Leslie pointed out that students fall into different categories based on the content. In one area, a student might fall into the advanced group, and then in the next, they might need a lot more support. AI can help create personalization for the students’ needs.

 

Real-World Uses Happening Now

Once we established why personalization mattered for every student, I asked Leslie for some examples of ways that teachers can use AI to help personalize the learning experience. Here are some of the things we discussed:

• Intelligent Tutoring Systems: These tools, such as Khanmigo, can provide immediate answers, explanations and allow for follow-up questions when students encounter barriers in their learning. They are useful in the classroom during project-based learning or at home for homework, offering support when a teacher or parent isn’t readily available. These systems can also tailor examples to a student’s interests or learning style, incorporating visuals and words.
• Teacher-Created AI Chat Systems: Leslie said that one teacher she interviewed used AI to create their own AI chat systems by feeding the tool specific resources and information. Students could then interact with these custom chats to brainstorm ideas, receive feedback and draw from teacher-approved resources, allowing for immediate feedback outside of class hours (like late-night questions).
• Student Work Analysis for Grouping and Activities: AI tools can analyze student work (after removing personally identifiable information like names, gender, or race) to identify common struggles among groups of students. This allows teachers to understand specific learning gaps and create tailored activities for those groups.
• Support for Students with Special Learning Needs: We also discussed some possible ways you could use AI to help create personalized learning for students with specific needs. While we haven’t tested some of these ideas, here are some that might work for your class.
  • Translation: AI can serve as a first step for translating teaching resources for English Language Learners, saving time for school staff who might then review the translation.
  • Dyslexia: AI could potentially translate text into dyslexic fonts, making reading easier for students with dyslexia.
  • General Tips or Strategies: AI can provide teachers with ideas and tips for supporting students with various learning needs or disabilities (such as suggesting an index card of a bright color to help students focus). However, these suggestions should be verified and tried out, as AI can sometimes “hallucinate” or provide information not supported by current research.
  • Cultural Context: AI could help teachers understand different cultural rules related to academic practices, such as varying definitions of plagiarism in different countries, which can be crucial when teaching international students.

 

Looking Ahead: What’s Emerging

Our conversation also touched on what’s coming next. Future AI-enhanced, online textbooks may allow students to pause for a video explanation, explore related concepts or revisit material in different formats that match their learning preferences.

These AI-powered online textbooks could offer a “3D learning experience” where students can type questions and get relevant videos, additional resources or deeper information on topics of interest.

While not necessarily AI-enabled, previous online textbooks already had interactive elements like videos and comprehension questions to progress.

 

Cautions and Considerations

While AI holds a lot of promise, there are risks and challenges to keep in mind.

Student data privacy is a top concern. Teachers should never input names or identifiable student information into public AI tools. Even when using AI behind the scenes, it’s essential to protect sensitive information and follow school policies.

Another concern is overuse. Not all learning should happen on a screen. AI should be a support, not a replacement for discussion, collaboration or human connection in the classroom.

Teachers should also be cautious not to let AI oversimplify tasks. Personalized support shouldn’t mean lower expectations. It should mean greater access to meaningful, challenging learning.

Leslie emphasized the importance of pairing Pillar 3 with Pillar 1 on creating cognitively demanding tasks.

 

How to Get Started

I ended this episode in the same manner I ended all of the episodes in this series: by asking Leslie what her advice on a first step would be for someone wanting to start using this pillar.

She said to use the KISS method that she teaches her students: Keep it simple, Silly!

Start by figuring out what AI tools you have at your disposal and how they can be used to create personalized learning.

More specifically, she suggested two easy things you can do now:

1. If you have English language learners in your class (or will when students get back in the fall), then try using AI to translate content into their language.
2. Survey your students. Learn about their interests, strengths and learning styles so that when you are ready to do more personalization, you have resources to help you.

 

More Support

This conversation is part of our five-part podcast and blog series based on SREB’s Guidance for the Use of AI in the K12 Classroom. Each episode focuses on a different pillar and is designed to stand on its own, whether or not you’ve read the report.

• Listen to the podcast
• Watch the video
• Download the full free report that inspired this series
• Download our AI Tool Procurement, Implementation and Evaluation Checklist

Finally, keep up with the latest from our Commission on AI in Education by signing up for our newsletter.

And make sure to come back next week, when we talk about Pillar 4: Creating ethical and efficient AI users of the future.