posted October 1, 2025

The headlines focus on the AI arms race between China and Silicon Valley—but the real transformation is happening in a place few expect: America's classrooms. And here's what those headlines miss: this isn't a story about technology replacing teachers. It's a story about educators discovering their superpower.

I've started to see AI as exactly that—my professional superpower. Not because it does my job for me, but because it amplifies my ability to do what I've always wanted to do: spark genuine curiosity, run meaningful experiments, and help every student discover what they're capable of becoming.

The question facing today's teachers isn't "Will AI replace me?" but rather, "How can I leverage AI to reimagine student learning, instructional leadership, and continuous school improvement?"

A 2025 Gallup/Walton Family Foundation survey found that 60% of teachers used AI tools during the 2024-25 school year—yet most applications remain surface-level: lesson plan generation or basic content summarization. I started there too. When I first opened ChatGPT, I saw it as a time saver. Generate a quick rubric. Summarize an article. Write a parent email.

But the more I engaged, the more I realized something profound: AI wasn't just answering my questions—it was sharpening them.

The transformative potential lies deeper: using AI as a collaborative thought partner for professional inquiry and data-driven instruction. Consider this emerging practice that I've begun integrating into my work at Kampus Insights—educators using AI to conduct classroom action research, systematic inquiry into their own teaching practice.

Instead of waiting for district-mandated professional development, teachers can:

• Analyze student work samples to identify learning patterns
• Generate differentiated instructional strategies based on assessment data
• Test pedagogical hypotheses through micro-experiments
• Reflect on practice with an AI coaching partner that asks probing questions

This shift from passive professional development recipient to active educational researcher represents a fundamental change in teacher agency. And it's available to every educator willing to ask better questions.

The most effective integration of AI in education occurs at the intersection of machine capability and human expertise—what I call "the sweet spot." Stanford researchers at the Graduate School of Education are actively studying how educators evaluate and use AI tools, emphasizing that successful implementation requires what they call "pedagogical reasoning"—the uniquely human ability to understand context, build relationships, and make ethical judgments.

Here's my lived experience of this partnership: When I draft a workshop outline for school leaders, I feed AI my initial ideas and ask, "What's missing? What assumptions am I making? How might a skeptical principal respond to this?" The AI doesn't replace my expertise—it stress-tests it. When I'm designing training modules, I ask AI to simulate teacher reactions or generate case studies based on real classroom dilemmas I've observed.

It's like having a thought partner who helps me refine my thinking in real time.

What AI can do:

• Process and analyze large datasets from student assessments
• Generate multiple versions of learning materials for differentiation
• Provide immediate feedback on certain types of student work
• Summarize educational research and synthesize best practices

What only teachers can do:

• Understand the whole child—their fears, dreams, and individual circumstances
• Build trust and psychological safety in learning environments
• Make nuanced judgments about when to challenge versus support
• Foster intrinsic motivation and love of learning

AI manages the transactional so we can focus on the transformational. Multiple McKinsey reports on automation and the future of work confirm that jobs requiring high emotional intelligence and complex problem-solving—including teaching—are among those least susceptible to automation. In fact, McKinsey research indicates that demand for social and emotional skills will increase significantly as automation handles more routine tasks. That's us. That's what makes teachers irreplaceable—and AI doesn't threaten that; it enhances our capacity to do that essential work.

As artificial intelligence becomes embedded in educational technology, we need to develop new professional competencies. Think of these as the pillars of powerful practice in the AI era:

1. Think Curiously and Strategically: Asking Better Questions

AI literacy in education isn't about coding—it's about questioning. AI is only as powerful as the prompts we give it. Teachers who master effective prompting can transform AI from a simple answer generator into a sophisticated thinking partner.

I learned this through trial and error. Early on, I'd ask AI: "Create a lesson plan on the water cycle." Generic prompt, generic result. Now I ask: "I'm working with a group of 5th graders who struggled on their last assessment with understanding the relationship between evaporation and condensation. Three students are reading below grade level, five are on track, and two are ready for extension. Design a 45-minute lesson that addresses these readiness levels while building both conceptual understanding and scientific vocabulary. Include formative assessment checkpoints."

See the difference? The more precise and context-rich the prompt, the more valuable the response. This requires us to articulate our pedagogical thinking clearly—which itself is a powerful form of professional reflection.

2. Connect Emotionally and Authentically: The Irreplaceable Human Element

Student engagement research consistently shows that caring teacher-student relationships are the foundation of academic success. AI can personalize content delivery, but it cannot provide the encouragement after a failed test, recognize when a quiet student needs checking in on, or celebrate a breakthrough moment in a way that builds lasting confidence.

As automation grows, empathy becomes our competitive advantage—the new innovation. Our ability to build trust, guide reflection, and see the whole child will always be our irreplaceable edge. AI can support efficiency, but it cannot feel. That's our domain.

As AI handles more routine tasks, we have more time for the relational work that changes lives. That's not a threat—that's liberation.

3. Adapt Continuously and Courageously: Embracing a Learning Stance

The pace of change in educational technology requires what Stanford psychologist Carol Dweck calls a "growth mindset"—the belief that abilities can be developed through dedication and learning.

Instead of fearing each new AI tool, I've adopted a learning stance: experiment, reflect, iterate. Every new application isn't a threat—it's an invitation to discover something I didn't know was possible.

Rather than overwhelming yourself, try this structured approach to continuous professional learning:

• Experiment with one new AI application per month
• Join professional learning networks focused on AI in education
• Document what works and what doesn't through reflective practice
• Share insights with colleagues to build collective knowledge

Let me give you a concrete example from my work. Last Spring, I was preparing a workshop on data-driven instruction for a district leadership team. I had my outline, my slides, my usual approach. But I decided to try something different.

I opened Claude and shared my draft agenda. Then I asked: "You're a veteran elementary principal who's skeptical about data meetings because they've historically felt performative rather than useful. How would you react to this agenda? What would make you tune out? What would earn your trust?"

The AI response was remarkable—not because it was perfect, but because it forced me to examine my assumptions. It pointed out that I'd front-loaded too much theory before getting to practical application. It noted that I hadn't built in time for principals to process their concerns. It suggested I start with a success story rather than deficit data.

I revised the entire workshop structure. The feedback from participants? Best data workshop they'd attended. Not because AI wrote it—but because AI helped me think more strategically about my audience's needs.

That's the superpower in action.

For educators ready to begin, here's your four-week journey:

Week 1: Exploration
Choose one AI tool (ChatGPT, Claude, Gemini, or education-specific platforms like Magic School AI). Spend 30 minutes daily asking questions related to your current teaching challenges. Don't judge the responses yet—just explore.

Week 2: Application
Identify one recurring task that consumes significant time (grading, lesson planning, parent communication). Experiment with using AI to streamline it, then critically evaluate the results. What worked? What felt off?

Week 3: Action Research
Develop one question about your teaching practice: "Why do my students struggle with X?" or "How can I better engage Y learners?" Use AI to help design a small-scale study, analyze results, and generate hypotheses for improvement.

Week 4: Collaboration
Share your learning with colleagues. Host a 30-minute lunch-and-learn. Be honest about what worked and what flopped. Document insights to build institutional knowledge.

I believe in AI's potential—but I'm not naive about the challenges. Professional adoption of any technology requires acknowledging legitimate concerns:

Privacy and data security: Schools must ensure AI tools comply with FERPA regulations and district policies. Never input identifiable student information without proper protocols. This isn't optional—it's essential.

Equity issues: AI access shouldn't create new divides. If only some teachers have the time, training, or technology to leverage these tools, we've just built another inequity into our system. Schools need strategic plans for professional development and infrastructure that reaches everyone.

Academic integrity: As students gain AI access, we need new approaches to assessment that emphasize critical thinking, creativity, and authentic application over rote reproduction. This is the harder work—but it's the work that leads to better learning outcomes anyway.

These challenges are real—but they're also solvable through thoughtful policy, professional learning, and community dialogue. We don't ignore them; we address them head-on while continuing to move forward.

The integration of AI in education isn't about replacing human judgment with algorithmic efficiency. It's about empowering teachers to become designers and action researchers—professionals who use every available tool to advance student learning.

Here's what I know for certain: we don't need to wait for the perfect policy or the ultimate AI solution. We don't need permission to start asking better questions, running micro-experiments, and testing what works in our unique contexts.

The educators who thrive in this era won't be those with the most technological sophistication. They'll be those who combine strategic use of AI tools with deep pedagogical knowledge, strong relationships with students, and a commitment to continuous improvement.

We are the implementers of our future. We don't have to wait for someone else to write the improvement plan or design the professional development. With AI as our collaborator, we can design, test, and refine the changes we want to see in our schools—one inquiry at a time.

Your next step: Identify one persistent challenge in your teaching practice. Open an AI tool—right now, today—and ask it to help you think through the problem from three different perspectives. Then take one small action based on what you discover.

That's how we spark curiosity.
That's how transformation begins—not with mandates or initiatives, but with curious educators asking better questions, one inquiry at a time.

The real superpower isn't AI. It's curiosity—the courage to ask, learn, and act. AI is simply the amplifier.

The future of learning will be shaped by educators who see AI not as a threat, but as an amplifier of their most important work: inspiring curiosity, nurturing potential, and helping every student discover what they're capable of becoming.

The power to elevate a nation of learners is already within us.

So don't wait for the future to shape you. Shape it today.

By Olivia Odileke created of Fe arless Educator Radio.  

Research on Teacher AI Adoption:

Gallup & Walton Family Foundation. (2025). Walton Family Foundation-Gallup K-12 Teacher Research. https://www.gallup.com/analytics/659819/k-12-teacher-research.aspx 

Kaufman, J. H., Woo, A., Eagan, J., Lee, S., & Kassan, E. B. (2025). Uneven Adoption of Artificial Intelligence Tools Among U.S. Teachers and Principals in the 2023–2024 School Year. RAND Corporation. https://www.rand.org/pubs/research_reports/RRA134-25.html 

Diliberti, M. K., Schwartz, H. L., Doan, S., Shapiro, A., Rainey, L. R., & Lake, R. J. (2024). Using Artificial Intelligence Tools in K–12 Classrooms. RAND Corporation. https://www.rand.org/pubs/research_reports/RRA956-21.html 

Lin, L. (2024, May 15). A quarter of U.S. teachers say AI tools do more harm than good in K-12 education. Pew Research Center. https://www.pewresearch.org/short-reads/2024/05/15/a-quarter-of-u-s-teachers-say-ai-tools-do-more-harm-than-good-in-k-12-education/ 

Research on Automation and Skills:

Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018, May 23). Skill shift: Automation and the future of the workforce. McKinsey & Company. https://www.mckinsey.com/featured-insights/future-of-work/skill-shift-automation-and-the-future-of-the-workforce 

Chui, M., Manyika, J., & Miremadi, M. (2018, March 23). How will automation affect jobs, skills, and wages? McKinsey & Company. https://www.mckinsey.com/featured-insights/future-of-work/how-will-automation-affect-jobs-skills-and-wages 

McKinsey & Company. (2019, December 9). Employee motivation in the age of automation and agility. https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-organization-blog/employee-motivation-in-the-age-of-automation-and-agility 

Stanford Research on AI in Education:

Stanford Accelerator for Learning. (2025). AI + Education Initiative. https://acceleratelearning.stanford.edu/initiative/digital-learning/ai-and-education/ 

Blair, K. et al. (2025). Teaching and tinkering: New Stanford project helps educators understand and use AI in their classrooms. Stanford Accelerator for Learning. https://acceleratelearning.stanford.edu/story/teaching-and-tinkering-new-stanford-project-helps-educators-understand-and-use-ai-in-their-classrooms/ 

Stanford HAI. (2024). How math teachers are making decisions about using AI. https://hai.stanford.edu/news/how-math-teachers-are-making-decisions-about-using-ai 

Growth Mindset Research:

Dweck, C. S. (2006). Mindset: The New Psychology of Success. Random House.