Eight students stood in line whispering to one another and fidgeting in excitement. A few of them had peeked into the windows to see what was new. One girl had a tiny notebook with a list. Most of the students had pulled out their phones to check key information. There was a buzz in the air that settled into an intense, quiet excitement. But this wasn’t a concert or a line to buy smart phones or the front gate of a sporting event. This was our first assigned library day of the school year. Being new to this school, I wasn’t sure what to expect. I had heard that this school kept reading fun and the library was the epicenter.
A boy walked up to me and asked, “What are you going to get?”
“I already have a book I’m reading,” I answered.
He shook his head. “Nah, Spencer, you’re going to get something. Everyone leaves with at least two books but no more than four.”
“Is that a rule?” I asked.
He laughed at the ridiculousness of the question. The rule? It was like making a rule that you have to eat at the buffet. It wasn’t a rule. It was just the way things were around here.
Right then, the librarian opened the door and the students streamed in . . . loudly. They scurried from display to display, reading the back covers and debating books. Our librarian seemed to embrace the noise as she called out specific books and named specific kids.
“Carlos! You’ll love this one,” she yelled from across the library.
Some of the students had been checking out books all summer but she had built up “release dates” for the first week of school.
The cynical side of me would have scoffed at middle school students getting this excited about books. After all, there is a cultural perception that reading is inherently uncool. As a child of the 1980’s, I always felt that those “READ” posters (you know the ones with Mr. T or Michael J Fox and the word “READ” in all caps behind them) backfired. It was like the Ad Council was trying too hard with its advertising. It made reading seem important and necessary but not fun. After all, we didn’t need signs imploring us to play or goof off.
But any cynicism I felt began to melt as I watched the sincerity of my students. They were pulling out their phones and reading the QR codes to get book reviews. They debated the merits of various series and, on occasion, mocked other students for their choices in books (something I would address in our first whole class meeting).
The library was like a candy store for the mind and my students were beyond excited to be there.
At one point, the librarian called students to an open space and reminded them of some strategies for finding the right book. She talked about reading sample pages, checking out the synopsis on the back of the book, checking the 3-Star Amazon Reviews (which often provides the most measured review of the pros and cons) and even looking at the book covers to see who they were marketing it toward.
This library experience defied the stereotypes of the stodgy, quiet library. However, I’d argue that many school libraries have a similar environment because librarians, as a whole, are finding innovating ways to get students excited about reading.
Looking back at it, our school librarian was a leader of an empowered community. She was a true expert in reading, curation, media literacy, and library science. But she never presented herself as the sole expert in reading. Instead, she built relationships with students and worked with them to help find texts that would connect with their interests. As a true curator, she read a broad variety of books and constantly explored new authors and genres with the hopes of helping students fall in love with reading.
She was also a master architect who designed systems that would empower students. She launched a buddy reader program where my eighth graders would read to first graders. She coordinated author visits and worked with teams of students to do book talks and book preview videos. In other words, she helped design the ecosystem of reading that would allow me, as the teacher to build a classroom culture of empowered readers. This was moment was a reminder of the power of authentic personalized learning.
What Do We Mean by Personalized Learning?
“Here’s the booklet. It tells you exactly what to do to help students get into the reading intervention software. Everything should stay inside the program. It’s cloud-based but everything has been downloaded, meaning they really can’t go onto the internet if they tried,” the district representative explained.
“What do I do?” I asked.
“Walk around. Monitor. If they have any questions, they’ll raise their hands,” he explained.
“But what about the discussions?”
“No, it’s personalized. There are no discussions,” he said.
“No literary circles?” I asked.
“No, it’s fully individual. They do targeted skill practice based on a pre-test. This is state of the art adaptive learning. That means they’ll get vocabulary practice and reading intervention work that targets their key deficiencies. You don’t have to do any assessment. I mean, yes, once a week, you’ll read the printout to them and talk about goals. But it’s driven by the adaptive learning software. You’ll get data on how they master every standard. You’ll get a fluency score. As they move up to higher reading levels, they’ll get badges.”
“And what do I do?” I asked again.
“Just monitor them,” he says. “It’s honestly the easiest class you’ll teach. This is the future of reading intervention. Personalized learning is finally a reality.”
As I implemented the program, I couldn’t help but feel that “personalized” was the wrong word. If anything, it felt impersonal. Students sat at computers doing digital worksheets meant to teach everything from phonics and blending to reading comprehension. They wore headphones as I walked around the rows and kept them on track. This was the first glimpse of adaptive learning fueled by artificial intelligence. A series of cryptic algorithms set the tone and pace of the learning and all I did was sit back and watch.
Three weeks later, I approached my principal with a new idea. After nearly a semester of this adaptive learning program (with a previous teacher and now myself), students weren’t reaching their goals. They were bored and frustrated.
So, I pitched a different idea.
We would head out to the library and choose books. Students would read silently each day and build up reading endurance. We would use recommendations from algorithms (recommended reading from Amazon) but also lean heavily on our amazing librarian. We would form literary circles and do shared read alouds. But we would also do five minutes of the adaptive learning fluency work. We wouldn’t avoid AI but we wouldn’t let the machines drive the learning. Instead, our focus would remain on the human connection.
Four weeks later, we compared this blended approach to the adaptive learning program. It turned out our students had higher reading scores than the district average for those in reading intervention. By focusing on motivation and building up reading endurance, students improved. By engaging in meaningful discussions centered on critical thinking, students improved in their overall reading compression.
I would never claim that adaptive learning programs don’t work. Nor would I claim that my experience is normative. But I share this story as a reminder that personalized learning has to begin with the person. It has to focus on voice and choice. It needs to include a human element.
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Personalized Learning or Adaptive Learning?
The line between personalized learning and adaptive learning is blurry. In some contexts, people use these terms interchangeably. In other contexts, people view adaptive learning as a part of personalized learning. Still others view adaptive learning as a part of customized learning. For me, personalized learning is human-centered and adaptive learning is machine-centered. I realize this will annoy certain folks in educational technology but these are the two terms I use.
| Personalized Learning | Adaptive Learning | |
| The Structure | Human-Driven: Personalized learning might use algorithms to inform the design but it is ultimately human-centered. | Algorithm-Driven: Students progress through pre-set curriculum and the AI adapts the levels to the skill level and interests of students. |
| The Learning Tasks | Authentic: Students engage in authentic problem-solving. There are opportunities to do creative work. | Programmed: Students don’t have as many opportunities to connect to the world or to solve authentic problems. Often, they work on targeted standards using digital worksheets. |
| The Grouping | Collaborative: Personalized learning requires interdependent student work. Even when students work on individual projects, they engage in peer feedback. | Individualized: Students work alone at a computer. The work is at their level and follows their pace. |
| Assessment | Varied: Students engage in self-reflections, peer feedback, and teacher assessments. They might even use AI but it’s simply one of many options. | Singular: Students might engage in a self-reflection as an assignment, but the AI is at the heart of the assessment process. It’s fast and efficient. Students get immediate feedback and the algorithm uses the assessment data to adjust the next learning task. |
| The Process | Messy: While personalized learning still leans into structures and scaffolds, the process is often messy. | Efficient: Adaptive learning tends to move efficiently with specific feedback and adjustments happening in the moment. |
| The Role of the Learner | Empowers the Learner: Genuine personalized learning focuses on learner agency. There’s a sense of freedom. | Engages the Learner: Adaptive learning is less about agency and more about providing targeted instruction. Students might get choices but they have no real voice in the process. |
| The Role of the Teacher | Active Facilitator: The teacher plays the role of instructional designer and often takes a step back as the “guide on the side” giving individual feedback or pulling small groups. But the teacher also engages in direct instruction and leads whole class activities. | Manager: The teacher might still do some tutoring or pull-outs by having the whole class use the adaptive learning program while they act as an active facilitator. But within most adaptive learning programs, the teacher is the manager of the system. Teachers review the data and make sure students are on task. |
There’s nothing inherently wrong with adaptive learning programs. I’ve seen these programs work well for certain types of skill practice – especially in world languages and math. But we need to be cognizant of the false promise that AI will provide personalized learning.
It won’t.
I’m already seeing bold promises of using generative AI for even better, more targeted adaptive learning. This newer generation will not only provide leveled work, but it will potentially create original math word problems, science examples, and non-fiction texts that connect to student interests while also being written at a student’s reading level with a focus on matching their skill level to the challenge of the task. Not only that, but students will also be able to interact with the AI like a tutor. They’ll ask questions and clarify misunderstandings.
While there might be a time and place for such adaptive learning programs, this feels like yet another iteration of a tech-centric model of personalized learning. What if we began with a human-centered view of personalized learning and then considered ways that we could use AI to augment rather than replace the human element?
Check out the following video I created about this very topic:
Using AI within Personalized Learning
At its core, personalized learning about empowering students with voice and choice. Adaptive learning programs tend to focus on the learner in a reactive mode but personalized learning places the student front and center. We previously explored how students might leverage AI in project-based learning. That’s an example of a more authentic approach to AI in personalized learning.
Another option might be a short inquiry-based activity. Inquiry-based learning is an educational approach that emphasizes the role of questioning and investigation in the learning process. It is a student-centered approach that encourages learners to actively participate in their own learning by asking questions, exploring ideas, and constructing knowledge through their own experiences.
In inquiry-based learning, the teacher serves as a facilitator and guide, rather than a source of information. Students are encouraged to explore topics of interest and to ask their own questions, rather than simply memorizing information provided by the teacher.
Inquiry-based learning starts out with a provocation. This is your initial starting point. You might give students a video to watch, a picture to see, a short reading to do, or a general set of parameters. The goal is to spark their curiosity. For example, in second grade science class, you might say, “Tell me what you wonder about natural disasters.” In social studies, it might be, “After learning about Ancient China, what do you wonder about?”
Students begin by posting their “wonderings” using the sentence frame, “I wonder _______.” From there, they create their own questions.
At a younger level, you might provide students with the question frames:
- What is the cause of __________?
- Why does ____________?
- What if ____________?
- When did _____________?
- Did _________ ever happen?
- Who did ___________?
- Where was ___________?
- How did ___________?
For older students, you might start there but add more challenging critical thinking sentence stems such as:
- What evidence can you present for or against __________?
- How does __________ contrast with __________?
- What is the significance of __________?
- What distinction would you make between __________ and __________?
- Why is __________?
- What are the pros and cons of __________?
- What are the advantages of __________?
At this point, students can start with an AI bot. They can ask questions and clarifying questions. While the information might have some biases and misconceptions, this can be a great starting place for students who are trying to learn the content. From there, they move into research. During the research, they can look at sources online. If a text is written at too complex of a level, they might copy and paste the text and ask an AI generator to modify the text to be at a different reading level (an idea we explore at the end of this chapter). Eventually, students will answer their questions and compose their own answers in the form of a blog post, a podcast, or a video. As they synthesize their information and craft a final product, they might use AI for aspects of their creative work. They might use an AI image generator within their slideshow. They might type up a blog post and then use a program like Grammarly for feedback on their writing.
The key idea here is that personalized learning remains personal. It’s built on student voice and choice. Inquiry-based learning begins with student curiosity. It includes collaboration. Artificial Intelligence is simply a tool that students use as they work through the inquiry process.
Using AI to Craft a Choice Menu
Another area where you might use AI is in crafting a personalized choice menu. Traditionally, choice menus allow students to choose how they will present what they are learning. It’s a great first step for students who aren’t used to having as much voice and choice in their learning. While choice menus are great, here’s a variation on the choice menu that goes beyond choosing topics and toward student ownership of the learning targets and resources. Here’s what it looks like:
| Learning Targets
Choose 1-2 learning targets that you haven’t mastered |
Resources
Choose 1-3 resources that you will use to learn about the content |
Product
Choose how you will demonstrate the mastery of the content |
| Example:
I can identify how animals adapt to their habitats I can explain how natural selection works |
Notice that with this choice menu, students are deciding either the topics, concepts, or skills and then deciding on their own resources and strategies before ultimately deciding on their final product. This typically takes 1-3 class periods, depending on the complexity of the learning targets and the end products. I found that this worked well in the following contexts:
- Early on in a unit, when they need to increase background knowledge
- Toward the end of the unit, when they need to own the intervention process
- When completing standards that don’t work as well with project-based learning or design thinking
- In the moments when there is a time crunch and they don’t have as much time to search for resources or where some of the online resources actually reinforce misconceptions
- If you are just making the leap into student-driven learning and you want to start with something that builds on student choice but doesn’t require a massive project
Here’s where the AI becomes a helpful tool. You begin with your own standards but then you can use AI generators to develop written examples of non-fiction texts that help students master those objectives. Again, these might be written at different reading levels, so students can access the content even if they struggle with text complexity. You can then find examples of online videos or images that might be useful as well. In some cases, students might also use AI as a question and answer tool.
When having students demonstrate their knowledge, you can select assignments and design rubrics using the AI tools. In the case of the science example, students would select the learning targets (which could be labeled and color-coded) and then click on the hyperlinked resources. After re-learning these concepts, students then share their learning in a video demonstration, a podcast, or a slideshow. For each of those options, they would have a hyperlink to an assignment page with directions and a rubric that you initially generated via AI and then modified to meet the needs of your students.
If this is a math choice menu, you might create math tutorials for the middle zone (resources) and then have it narrow down the options to specific math problems that they then solve. These can exist on a Google Form where they start with the learning targets as page 1 which then leads to resources on page 2 and then they can click on a link of optional math problems where they attempt to solve it and get immediate feedback.
Notice that this is similar to the adaptive learning program. However, instead of having a computer choosing the options, students get to decide. While this choice menu example is individual, you might have students do this in small groups as a targeted intervention. Here, the AI works on the back end, but the process is more social and human.
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