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In 1899, a reporter named E.P. Ingersoll wrote about the possibility of a bus line between Chicago and St. Louis, concluding, “The notion that electric vehicles, or vehicles of any other kind, will be able to compete with railroad trains for long-distance traffic is visionary to the point of lunacy.”

Ingersoll, of course, was wrong.

The car radically reshaped transportation and, ultimately, modern civilization, albeit with physical, psychological and societal costs. Cars make many things easier but also created traffic, pollution, risk of death from accidents, the suburbs and .

Today, thinking that artificial intelligence won’t play a significant role in education is like thinking the automobile wouldn’t change transportation. What we must do now is begin to anticipate the costs we might accrue. One such cost is that the adoption and use of AI could look vastly different in schools and communities with high-poverty rates than in those set in affluent places.

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Imagine one town with modern roads, accessible driver’s ed and an array of vehicles. Imagine another town where cars drive on a muddy road, making a mess of street and sidewalk alike so that everyone’s day gets a little difficult. This is the “smart use divide.”

The work to improve education outcomes for all American children is most often the work to improve education outcomes for those with the fewest opportunities and resources. If AI can in fact live up to its promise to supercharge education and maximize efficiency in operationsit’s reasonable to believe this will first, or even mostly, happen in the schools and communities that have the opportunities and resources to use AI in smart ways.

It’s not the same as asking whether schools in low-income communities are using AI. Right now those schools are likely using some form of educational technology, some teachers may be using ChatGPT or other tools to create lesson plans, and students are absolutely using AI to cheat (because all students, at all income levels, are doing this).

The Smart Use Divide is especially problematic because it grows in both directions. Poor AI use will lead to less learning for the students we might worry most about — negative student use of AI such as cheating may become pronounced or go unchecked, and positive student uses of AI such as high-quality tutors or other personalized learning approaches are less likely to be correctly applied.

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When it comes to educators, it is well established that the least-experienced teachers are likely to end up at the most challenging schools. Those teachers are likely to have less experience or ability to incorporate AI in effective ways.

High-poverty districts are also providing fewer training opportunities on AI. Only 39% of high-poverty districts reported providing teacher training on AI in the fall of 2024, compared to 67% of other districts.

If this sounds harsh, it’s only because this is the lesson we have learned through previous rounds of technological innovation. High-poverty schools were the last to have good internet connections, then the last to move from broadband to wireless connectivity. In 2024, one survey found that high-poverty schools are significantly likely not to vet any of their ed tech products and their students are most likely to have “unsafe apps with digital ads…and behavioral ads.”

Teachers in high-poverty schools already say that ed tech tools are effective for “supporting practices related to learning new content, and practicing and assessing new skills.” But their counterparts in affluent schools say these tools are effective for “supporting student collaboration and research.” In other words, affluent students are using tools to augment learning while lower-income peers are using tools to learn the material in the first place.

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Students’ belief in their own abilities also reinforces and furthers this divide. Students with “low self-efficacy are inclined to rely on AI” while students with confidence in their academic abilities “were selective in AI reliance.” We should expect this pattern to repeat with AI-fueled technology. Sadly, relying on AI for learning may well result in less learning happening at all.

Can we minimize the smart use divide? Here are three ideas that can be implemented now. First, all schools should draw a hard line at the use of generative AI in student schoolwork.

At the high school level in particular, this may mean in-class writing, or using old-fashioned blue books for written exams as a number of colleges have done. The advent of new technology is not a sufficient excuse to allow students to abstain from acts of learning, including writing.

Second, schools should support effective teacher use of AI. Teachers who use AI tools at least weekly are already saving almost six weeks of work time over the course of the year.

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But than one-quarter of teachers are only using AI tools once a month or less. Teachers need training and support to discover the time-saving and quality-enhancing benefits of new AI tools, while ensuring that the very human act of providing feedback and delivering great lessons remain human endeavors.

Finally, even as automobiles were becoming increasingly prevalent, a solid wagon could still transport a farmer and his goods to market. America knows how to effectively educate students with rigorous, engaging coursework.

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We’ve done so for centuries. Excellent education has never been equitably distributed, however.

State, district, and school leaders should pay attention to and engage with AI, but primarily focus on student outcomes. Look to high-poverty districts that are showing significant academic growth, like Somerset ISD near San Antonio, Texas. In spring 2025, 68% of Somerset students scored on or above grade level on the state Algebra I examcompared to 47% of all Texas students.

Find a school or district with similar demographics to yours that is outperforming you, and figure out what’s working for them. Maybe it’s about smart AI use — or maybe it’s a laser focus on instruction or an ongoing commitment to tutoring. At the same time, don’t abandon what works for the seductive appeal of emerging technology.

Underlying the excitement about AI hovers a quiet implication that these are tools the education sector has been waiting for, that only with these tools will student learning finally reach long-desired heights. But nobody needs AI to make schools work. Indeed, if a school is failing without any AI tools, there’s no evidence to suggest the adoption of those tools will address the reasons causing the school to fail.

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The smart use divide will grow if schools allow for poor AI practices to take hold and if schools allow for the thrill of new AI tools to distract from the core work of teaching and learning. Nobody wants to be the last one holding the buggy reins while everyone zooms along at 60 miles-per-hour, but neither will building stoplights without first paving the road get you to your destination any faster.

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