Across the country—and around the world—education systems are stretched. Teacher shortages, particularly in rural and underfunded communities, are leaving students without access to quality instruction. And the result is all too familiar: existing gaps in achievement continue to widen.
But while these challenges aren’t new, the tools we now have to address them are. Artificial Intelligence (AI), once a futuristic concept, is quickly becoming a practical, scalable solution for some of education’s most pressing problems. Not by replacing teachers, but by supporting them—and by helping schools provide personalized, equitable learning at scale.
One of the most exciting possibilities with AI in education is its ability to ease the burden on teachers while improving access for students. In districts where finding enough qualified educators—especially in STEM fields—is nearly impossible, AI can help bridge the gap. It can assist with lesson planning, supplement classroom instruction, and provide one-on-one support to students who might otherwise fall through the cracks.
Just as importantly, AI systems can adapt to individual learning styles. Some students might respond well to structured, no-nonsense feedback, while others need encouragement or a coaching tone. Intelligent tools can adjust their approach based on how each student best absorbs information. That kind of personalization, previously limited to private tutoring or highly resourced schools, could soon be available to students everywhere.
Students don’t just fall behind because they lack motivation—they often fall behind because the system wasn’t designed with them in mind. Whether it’s a student with an undiagnosed learning difference, a language barrier, or chronic illness, our current education model tends to teach to the average.
AI can help change that. Tools designed for “empathy at scale” give students access to judgment-free help, anytime they need it. For a student too shy to raise their hand, or one who’s been made to feel less capable, this kind of support can be transformative. AI doesn’t get tired, doesn’t lose patience, and—when implemented thoughtfully—can offer the kind of consistent, adaptive support that even the best teachers can’t always provide to every student.
That’s not just good pedagogy. That’s a shot at real equity.
One of the biggest fears surrounding AI in schools is that it will eventually replace human teachers. But that narrative misses the point.
There simply aren’t enough teachers right now, especially in high-demand subjects. AI isn’t taking jobs—it’s picking up slack. And even the most advanced systems can’t do what great teachers do: notice when a student is struggling emotionally, de-escalate conflict between classmates, or model empathy, curiosity, and character.
What AI *can* do is save time—on grading, content creation, and even answering routine questions—so that teachers can focus more on teaching, mentoring, and supporting students in ways only humans can.
There’s valid concern about whether AI can be trusted to provide reliable information. After all, generative AI is known to confidently provide wrong answers. But those issues aren’t deal-breakers—they’re engineering challenges.
Techniques like retrieval-augmented generation (RAG), careful prompt design, and fine-tuned models can drastically reduce errors. In fact, with proper implementation, AI can be as reliable as a well-curated textbook—and far more responsive.
Rather than avoiding AI out of fear, we should be teaching students (and educators) how to work with it critically. That includes understanding its limitations, verifying sources, and applying a healthy dose of skepticism—skills that are essential in today’s information landscape.
Prohibiting AI use in schools isn’t just ineffective—it’s a missed opportunity. The reality is that future workers will be expected to use AI tools just like today’s workers are expected to know spreadsheets or email. A student who knows how to use AI responsibly will have a clear edge over one who doesn’t.
Banning AI also risks wrongly accusing students of cheating, while failing to teach them the digital and media literacy they need. Instead, schools should focus on *how* AI is used: encourage its use in brainstorming, revision, or practice—while still ensuring that assessments measure real understanding.
Like it or not, AI is here. The question is whether we’re preparing students to thrive with it.
One promising development in AI-enhanced education is the use of digital avatars—visually expressive, emotionally responsive characters that can tutor, guide, and simulate real-world interactions. Research shows that students, especially those with autism Autism Spectrum Disorder or social anxiety, often feel more comfortable engaging with an avatar than a live person. The predictability of an avatar’s behavior can make learning feel safer and more approachable.
These avatars can also support multilingual learners, offering instruction in several languages simultaneously. And they can be programmed with different personas to suit different learners, making the experience feel personalized—even in large classrooms.
Beyond tutoring, avatars can be used for simulations, role play, and immersive learning experiences. Imagine practicing a historical debate with a Thomas Jefferson avatar, or simulating a clinical interaction in nursing school. These tools don’t just deliver content—they create experiences.
Of course, all the AI-powered tools in the world won’t help if students can’t access them. We can’t talk about AI as a force for equity without acknowledging the ongoing disparities in internet access and device availability.
Students without Wi-Fi or laptops at home can’t benefit from 24/7 learning support. That’s not an AI problem—that’s a policy and funding problem. If we want AI to close opportunity gaps, we need national and local investment in infrastructure. Devices and internet connectivity should be treated as essential school supplies.
We also need to invest in AI literacy—teaching students not just how to use tools, but how to understand what’s happening under the hood. That includes recognizing bias, questioning outputs, and knowing when human judgment is required.
Deploying AI in classrooms brings real responsibility. Tools must be age-appropriate, culturally sensitive, and protected against misuse. That means strict content filters, well-designed prompts, and safeguards that prevent AI from “going off script.”
It also means acknowledging bias in training data. Most AI is trained on publicly available text, which often reflects dominant perspectives while marginalizing others. Educators and developers need to proactively counteract that by diversifying sources and building more representative models.
And finally, we must respect intellectual property. If AI tools incorporate proprietary material—like illustrations, assessments, or curriculum content—they need to do so ethically, with proper licensing and attribution.
Equity in education isn’t something AI will automatically deliver. It depends on how the technology is built, who has access to it, and what values shape its use.
Governments, school systems, and companies all have a role to play. Tech developers should prioritize affordability and accessibility, rather than chasing profit at the expense of impact. Policymakers must understand the tools they’re regulating—not just in theory, but in practice. And school leaders should adopt clear, thoughtful guidelines that balance innovation with student safety.
The stakes are high. But so is the potential.
AI won’t solve every problem in education. But it can help us move faster and more effectively toward a system that’s truly inclusive, personalized, and fair.
With the right guardrails and the right intentions, AI can support the educators we rely on, uplift the students who are too often left behind, and help close opportunity gaps that have persisted for generations.
This isn’t about shiny new tools—it’s about rethinking what’s possible. And doing it with care.
By Michelle Collins, Chief Revenue Officer, CodeBaby
As Chief Revenue Officer of CodeBaby, Michelle Collins oversees product development, sales, and marketing, working closely with teams across all departments. With a keen eye for innovation, she continuously evaluates new technologies and identifies how they can be integrated into CodeBaby’s products and services to deliver the most advanced, useful avatars in the market.
