Hello everyone,
My name is malak, and I’ve been teaching middle school science for about ten years now. If you've been in the profession for any length of time, you know the feeling I’m about to describe.
You’re standing in front of your classroom, looking out at a sea of 30 faces. In the front row, you have a student who could probably teach the lesson herself. In the back, you have a student who is still struggling with concepts from two years ago. And then you have everyone in between.
For years, my biggest professional struggle has been answering a single question: How do I teach all of them at once? The traditional model—lecture, worksheet, test—always felt like I was aiming for the middle and inevitably losing the students at both ends of the spectrum.
Then, the AI wave hit. Every conference, every professional development email, was filled with buzzwords like "personalized learning," "adaptive technology," and "machine learning algorithms." Honestly, my first reaction was skepticism.
It sounded like another expensive ed-tech fad that would promise to solve all my problems but would end up being more work than it was worth. But that feeling of not reaching every kid was gnawing at me, so I decided to dip my toe in the water. Cautiously.
What I’ve discovered over the past year isn’t a magic bullet. But it has fundamentally changed the way I approach teaching.
My First Experiment: Taming the Content Beast with Adaptive Platforms
The first thing I tried was what they call an "adaptive learning platform." The idea is simple: the software gives students questions and, based on their answers, adjusts the difficulty in real-time. If they're acing it, it gets harder. If they're struggling, it offers simpler problems or even tutorials.
I decided to try one for a unit on ecosystems. I still gave my main lessons, but I replaced the traditional homework worksheets with this platform. The first week was a bit clunky. But then, something amazing happened. I was walking around the room and saw one of my brightest students, who was usually bored, completely absorbed in a complex simulation about invasive species—something far beyond our standard curriculum. A few minutes later, I sat with a student who had always struggled with food webs. The platform had identified his specific misunderstanding (he was mixing up producers and consumers) and was giving him a mini-lesson with simple examples until a lightbulb went on over his head.
For the first time, I felt like I wasn't just teaching to the middle. I was giving every student the specific challenge or support they needed, right when they needed it. Platforms like DreamBox or ALEKS aren't about replacing me; they're about being my assistant, handling the differentiated practice so I can focus on the bigger picture.
Grading is a Nightmare. AI Can Be a Dream for Feedback.
Let's be honest: grading is the worst part of our job. It's time-consuming, and by the time students get their papers back, the moment for learning has often passed. I was curious if AI could help, not by slapping a grade on something, but by improving my feedback.
I started using an AI tool to review the first drafts of my students' lab reports. I didn't let it grade them. Instead, I used it to check for common writing errors and, more importantly, to generate questions. For a student who wrote a vague conclusion, the AI might suggest I ask, "What specific data from your experiment supports this conclusion?"
This changed everything. My feedback became more targeted and immediate. Instead of spending my entire Sunday with a red pen, I was spending my time having meaningful conversations with students about their scientific arguments. The AI handled the low-level stuff, freeing me up to be a real coach. It's not about outsourcing the assessment; it's about making the feedback loop faster and more effective.
A "Tutor" for Every Student: The Power of AI Assistants
In every class, there are students who need just a little more one-on-one time than I can possibly give. This is where I've started to see the potential of "intelligent tutoring systems." Think of them as a patient, 24/7 virtual assistant.
I introduced one to my class as an optional resource. A few students started using it to review concepts before a test. But one student, an English Language Learner, started using it in a way I never expected. She would ask it to re-explain complex topics like photosynthesis in simpler English, or even translate key vocabulary for her. It filled a gap that I, as one person, couldn't always fill in the moment. It didn't replace our classroom interactions, but it gave her a level of support that helped her keep up and build confidence. These tools are becoming powerful allies in creating a truly inclusive classroom.
The Big Picture: Finally Seeing the Patterns in the Data
One of the most surprising benefits has been seeing my class in a new light. All these tools collect data, and while the term "data analysis" sounds intimidating, it's really just about spotting patterns.
After a few weeks of using an adaptive platform, I looked at the class-wide report. I noticed that nearly 70% of my students were struggling with interpreting graphs. It wasn't something I had picked up on from my traditional tests. This single insight was invaluable. I was able to pause my planned curriculum and spend two days doing a deep dive on data literacy. It was a problem I never would have seen so clearly without the help of the technology. This isn't about complex machine learning; it's about having a better map of where my students are, collectively and individually.
The Hurdles are Real, But So is the Payoff
Now, I want to be clear: this wasn't a seamless, easy process. There were days the Wi-Fi was down. There were budget conversations. There was a learning curve for me and my students. And there's the constant, nagging question: are we balancing this technology with real, human connection?
What I've learned is that AI is a powerful tool, but it is just that—a tool. It can't replicate the encouragement I can give a student who is having a bad day. It can't lead a Socratic seminar that sparks genuine curiosity. It can't build the relationships that are the absolute foundation of our profession.
My goal isn't to create a "blended learning environment" or to check a box on my evaluation. My goal is to use these tools to handle the parts of my job that are repetitive and mechanical, so I can free up my time and energy for the parts that are uniquely human.
The future of AI in education isn't about robot teachers. It’s about giving human teachers the superpowers they need to finally do what we all got into this profession to do: to see every single student, to challenge them, to support them, and to help them grow. And for the first time in a long time, that feels more achievable than ever.
