How teachers can use AI to listen, reflect and build a math class community

I did not expect a mathematical newspaper entry to change my point of view. But while I was scanning in my students' reflections that morning, an answer stopped me on my traces:

“It is more important to me than my teacher sees me as a person than if I receive all the correct answers.”

A student, whom I will call Jason, had been in my class for months – calm, polite, barely noticeable. Do not fail, not prosperous. Just … there.

Jason's words reflected what many students feel, but rarely say. While I was reviewing other newspaper entries, I discovered an echo of voice expressing uncertainty, quiet resilience and a desire to be heard. I highlighted themes and let their words settle down, but as the answers accumulated, I needed help to see the situation as a whole.

It was at this moment that I turned to artificial intelligence (AI), using it to help summarize the newspaper entries – without replacing my judgment but sharpening it. Chatgpt has surfaced the models that I could have missed: anxiety about the taking to speak, the appreciation of kindness, the importance of being seen. The AI ​​did not give me a summary of the answers – it gave me a perspective, revealing what my students told me between the lines.

Too many students enter mathematics lessons bearing incalculable stories – on race, failure, shame, invisibility. And mathematics, with its rigid structure perceived on the right or embarrassment, often leave little room for the disorder of human being. Reflective reviews and AI have made this space. They reminded us that learning is emotional before it was cognitive.

Some see AI in education as a threat to authenticity – something that could replace significant learning, weaken rigorAnd erode relations. A large part of the conversation focuses on cheating fears and weakened critical thinking. But according to my experience, the opposite is possible. When it is used thoughtfully, AI does not dehumanize the classroom – it remanates it, helping us connect to the students' emotional landscapes and respond with more clarity and compassion.

For educators who explore how to go from algorithms to empathy, here is what I learned:

Use AI as a reflection partner to surface the trends in the voice of students. I presented reflexive journals with prompts like “How do you see you in mathematics?” And “where could mathematics be important in your life?” When the answers accumulated, AI helped me identify the emotional lines – which the students feared, appreciated and had to feel seen. It did not analyze the feelings for me; He highlighted models through dozens of responses, allowing me to respond not only as a content of content, but also as a auditor who could meet the collective needs of the class.

Let the AI ​​manage the groan so that you can do heart work. After the AI ​​helped me identify themes like “I don't feel intelligent, but I try stronger than people know it” and “I am not the only one to ask for help”, I shared this anonymous information with my class. The heads harch their heads. The room moved. These reflections did not aim to repair the students – they aimed to make space where vulnerability felt safe and mathematical identity could evolve.

Design with AI – Not for that. I did not start by asking what AI could do, but rather “What do my students need to feel seen, disputed and supported?” It was only then that I explored how technology could help me meet these needs more thoughtful and efficient. The tools followed the vision, not the other way around.

Treat AI as a co-designing, not a substitute. The AI ​​will never replace personal connections at the heart of teaching, but that can help me see what could be missing in the daily chaos of the class. This partnership allows me to combine technological information with relational knowledge that comes only from the knowledge of my students.

The day after reading the entry of Jason's Journal, I welcomed it more intentionally and I shared that I had already felt the same thing to be considered a person first. It was a small signal: I see you. This breakthrough has emerged from the recognition that community construction in a mathematics class does not require groups or developed groups. Sometimes it starts with something quieter: to give students of space to examine their relationship with mathematics itself, then use AI to help us listen to what they tell us more deeply.

A week later, Jason dwelled after lessons. “Thank you,” he said. “For, like, share with me.”

This two -second moment fell for something opening – for both of us. Because behind each silence is a student waiting to be seen. And sometimes, the most powerful data that we can use is not a test score or a reference – it is a journal entry, a sign of recognition or a silent “thank you” visible with the help of AI, reminding us why we teach.

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Al Rabanera teaches mathematics at Vista high school in Fullerton, California. It's a 2025-2026 Teach Plus Leading Edge Educator Fellow.

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