What If We Don’t Use AI?

The danger is not in not using AI, but in ignoring AI.

It seems that AI is everywhere, and every organization is asking whether and how it should be using AI. Some companies have gone so far as to mandate AI usage, and AI proficiency is becoming a skill new hires are increasingly quizzed on.

The current form of AI has the potential to change society in much the same way that the personal computer and the internet did. In the context of education, it has the potential to change how and what we teach, and what it means to prepare students for a life beyond K-12.

AI has been slowly making its way into professional workflows in subtle ways to enable individuals to do more or work more efficiently. AI image generation tools are allowing studio producers to iterate on their own and bring a more refined concept to their artists. Radiologists are increasingly using AI tools in medical imaging to assist in detecting abnormalities.

And unquestionably, some of the AI tools that have appeared in the past few years are game changers. We can mine the internet with natural language using ChatGPT – when it doesn’t hallucinate. Generative AI tools are allowing creators to create amazing imagery and video – and causing some consternation among Hollywood actors. Self-driving cars can give us a new level of freedom of getting from A to B – though their robotaxi incarnations are presently causing traffic gridlock when they get confused. As we rush to realize grander ambitions for AI, we’re seeing it is a double-edged sword. And because the technology it’s built on has different properties than the computation and automation we’ve become accustomed to, we don’t always assess its harms until the sword is descending upon us.

Looking to the near future, agentic AI opens the door for AI tools to act autonomously – today with baby steps. But steps that will no doubt lead to greater strides as agents become more capable and we become more accustomed to working with them. And artificial general intelligence – the point at which AIs exceed human “intelligence” in all areas – was once thought to be a theoretical fantasy, then considered achievable perhaps in the 2030s, but some are now claiming it will be reached in the next few years. And in many respects, we’re just out of the starting gate.

With all the buzz and promise, it seems odd to ask What if we don’t use AI? But because AI is a new and rapidly evolving area, thinking through What if we don’t use AI? is just as important as How should we use AI? and Where should we use AI? and work. And circling these questions is: What should we be doing?

Backgrounder: What Is AI?
Backgrounder: What Are AI Tools?

Not your typical advancement

In the computer age, we’re used to tools which allow us to do things more efficiently. They may be faster, improve accuracy, or offload some of our work. As computer programs, they work in a defined way and (more or less) do what we expect them to do.

AI tools fall into another class. They can seemingly imbue their users with new skills. For example, in TV studio environments, it was typical for producers to work with artists to create artwork. The producer described what they wanted, the artist sketched, and there was a lot of back-and-forth. Now we are seeing producers work with generative AI tools to create artwork. At least today, these are not production-grade. The AI tool has not turned the producer into a production artist. But it has turned the producer into a junior artist. The producer’s artwork is shared with the artist for refinement. It cuts down the back-and-forth. It also means that artists are now expected to work at a higher level – they’re no longer doing a lot of rough sketches, but are instead transforming the producer’s draft into a production art that “speaks” to an intended audience (something which the producer may not be capable of even with the help of AI). AI tools are like cognitive prosthetics. They upend the playing field – individuals who learn to use these tools gain an advantage and may outperform those who do not on various metrics. The lines between roles become blurred and the skills dynamic, resulting in roles being created, eliminated, and altered.

Fortunately, because AI is a young and rapidly evolving field, the risks of not making immediate use of it are low. In fact, one can take a cue from Apple, which, rather than rushing to spend vast sums of money on AI and data centers, has taken a more measured approach. Apple has rarely been at the fore of providing bleeding-edge technology in its products. It is usually elegantly late, with much thought and care given to how technologies are expressed in its products.

If AI is not used in education, at one level, nothing will happen. The status quo will be maintained. Teachers will continue to teach, and students will continue to learn. The risks become more apparent down the road, as tools mature and other school systems are reaping the rewards of adopting them.

AI Fluency and Competency

Even if AI ends up not being used, both teachers and students need to be able to be fluent in it – they need to be able to discuss it with peers and those who are experimenting with AI tools, and to be able to understand how the field evolves. AI comes with a whole new set of terms and concepts. Some, like “AI hallucinations” are becoming mainstream. Others like “context window” and “attention mechanism” remain mostly in the technical realm, but may pop up in non-technical literature. Understanding them allows us to work better with AI. (A “context window” can be thought of as an AI model’s short-term or working memory. The context window has a limited size, and how you interact with an AI model affects what ends up in it. Managing the context window is an example of a skill that distinguishes novice and expert AI tool users.)

At an organizational level, everyone requires some level of AI fluency. Everyone needs to be able to meaningfully participate in discussions about AI tools and their potential uses.

But an organization’s decision-makers and thought-leaders also need access to a deeper level of knowledge.- a level that cannot be achieved by book learning but is gained by hands-on use of AI tools – with direct experience in the successes, pitfalls, and outright failures that result from their use. This level of knowledge is particularly valuable given AI’s novelty and the rate at which it is changing. Decision-makers and thought-leaders don’t necessarily need to possess this knowledge themselves, but they do need trusted individuals who can provide it to them.

In high-tech corporate organizations, the Office of the Chief Technology Officer or equivalent may provide this sort of direction. But in other organizations, the same knowledge can be obtained by allowing a few individuals comfortable with walking the bleeding edge to gain a deep understanding of what AI can do. Their experiences can then shape AI usage across the organization. Their deep fluency imparts an understanding of not only what an AI can and cannot do for the organization, but also why. This deeper level of understanding allows them – and through them their leadership – to extrapolate how AI tools might be used in other areas, how they might be adapted and improved, and to engage with those developing tools.

The risks of not developing fluency in AI are subtle and pervasive. AI changes the way we interact with computers (as evidenced by ChatGPT), the way we search for answers (again ChatGPT), and how tasks can be automated (the emerging field of agentic AI). Interacting with an AI is not like interacting with a typical computer program. You need to know how to talk to it. You need to know when (and to what degree) you can trust its answers, and when to call a result a hallucination. You need to know where AI is useful, and where it is not. And the rapid pace of advancement means that the answers to all of these questions are very dynamic. A task that an AI could not handle well last year, it could excel at next year. Or, next month.

AI as an enabler

AI tools can enable both teachers and students to become proficient in areas they were not before. A teacher who has difficulty interacting with a certain student directly may find that the student interacts well with a certain type of chatbot teaching aid. The chatbot becomes an intermediary and tool, bridging the two. Similarly, students who are unable to draw but have artistic potential may use generative AI tools to create works and refine their talent. The criterion shifts from “can you wield a brush” to “can you create a piece that conveys your message?”We step from the mechanics of a lesson or task to its core.

In all these cases, AI is filling in a gap. We need to take a step back and look at the big picture. It’s true that a student or teacher who relies on an AI tool as a crutch will not perform well without that tool. However, if their capacity is fundamentally changed with that tool – and the tool will be readily available when needed – does it matter?

Is a student who uses a calculator proficient in math? The answer has changed over time. Today we use calculators (or computers) routinely and being able to do multiplication without them is simply not a valued skill. Performing mathematical calculations are inevitably part of another skill – preparing taxes, managing inventories, or balancing checkbooks. We value those higher-level skills and easily ignore that an accountant proficient in the tax code and finding deductions may be woefully deficient in long division by hand. We trust his tax program renders that skill unnecessary.

If we do not use AI tools in education, we lose we lose the new avenues that AI prosthetics open for both teaching and learning. Teachers may not be able to convey lessons in as meaningful a way to each student. A student’s lack of a certain skill may prevent them from developing adjacent or higher-level skills.

New Ways of Learning

Not using AI allows the status quo to be maintained, and no immediate harm is caused. This is a rather comforting position, and many may be tempted to keep doing what they have been doing. But not using AI also means we are not availing ourselves of new ways of teaching.

As an example, chatbots can allow instruction to be performed in a conversational manner rather than textbook study/homework. Conversational learning has advantages over textbook instruction, a primary one being that the student is an active participant in the conversation. Information is retained and incorporated into the student’s mind as it is actively used. For some students, this may result in superior comprehension and retention compared to problem sets or other tools traditionally used to reinforce textbook learning.

Conversation between a student and chatbot also affords the possibility of more personalized instruction. A chatbot can assess a student’s knowledge during conversation. It can then spend more time on aspects of a lesson that the student is unfamiliar with or struggling with, while spending less time on areas the student appears to have a good grasp of.

Instructional AI tools may also be able to tailor instruction for each student. Subject material could be altered to a particular student’s interests while the core of the lesson plan remains the same. Examples, parables, and the “surface topic” of problems may be substituted to be more “interesting” or relevant, making them more meaningful and easily integrated into a student’s knowledge base.

The use of chatbots and dynamic lesson plans would be a novel mode of instruction that might previously be available only in very small classes, where 1:1 interaction between a student and teacher is possible. Even then, the load on a single teacher could become prohibitive.

But despite the potential advantage, hurdles must be overcome. Instructional chatbots must be developed – chatbots that are focused on instruction rather than general conversation. A means of constraining a chatbot must exist so that it has some leeway in how it adapts a lesson plan, but the lesson itself remains what a teacher intended. As with all tools used in an educational environment, chatbots must be constrained from venturing into areas that parents (or general society) might object to. And, a teacher must be able to trust that the chatbot will faithfully render a lesson without hallucinating or otherwise causing harm.

The Fallacy of Scores

Some attention needs to be given to whether adopting AI will help or hurt school systems in their scoring. Goodhart’s Law is particularly relevant here – When a measure becomes a target, it ceases to be a good measure. Tests, scores, and metrics are initially valued because, when they were created, the scores or metrics were well-correlated with some proficiency. The score becomes a proxy for a more thorough evaluation. But the relationship is correlational and not causal.

Goodhart’s Law can be prescient in many ways – the pressure to be measured well means there is a tendency to lose sight of what is supposed to be measured. There may be pressure to game the system so that scores are excellent, yet the proficiencies they are supposed to correlate with are absent.

AI and AI tools are disruptive to existing tests and scores because they fundamentally change the landscape. The context of the system being measured is fundamentally changed, so tests may no longer correlate well with the proficiency they are supposed to assess. In some cases, the proficiency itself may no longer be valued.

This does not mean measures should be abandoned, but when AI tools are (or are not) brought to bear, their impact on test scores needs to be evaluated in a larger context. What a test is supposed to measure should be asked, as well as whether that goal is being achieved, even when scores are low.

Overreliance on AI tools.

There are studies and papers making the rounds proclaiming a particular risk in using AI tools – that they erode the skills in human that they themselves provide. In the context of education, teachers may become less proficient at teaching when they use AI tools. At some level this argument is the same alarm sounded regarding the use of calculators to do math and computers for the varied purposes we have put them to. But we live in a world today where calculators are ubiquitous (most likely even on our phones.

However, as with any aid, using AI tools is not without its risks, and we must be cognizant in our use of tools so that we reap their benefits while avoiding their pitfalls.

There is a risk of losing touch with fundamental knowledge if AI tools are used exclusively. The difference is using an AI tool to accelerate a task vs using it to do something you don’t know how to do. If you know how to do a task, you have some knowledge of the underlying steps and which approach yields the best results. These steps can be time-consuming and require going through vast amounts of information – the types of tasks which AI tools (or regular computer programs) can assist with, so that you can focus on higher-level tasks. But with your knowledge of the task, you can assess if the tool is doing things in an appropriate manner and the quality of the result. Ideally, the tool would proceed in the same manner and produce the same result as you would.

There is a risk of losing touch with fundamental knowledge if you rely too heavily on a tool. Quite simply, our understanding of the domain starts to become outdated, and we may forget certain aspects of it. One way to keep our core skills up-to-date is to rely on tools most of the time, but to periodically do things ourselves. In doing so, we remind ourselves of the domain and also confront any changes. We can also periodically do a deep inquiry into how a tool arrived at a result. If the tool took a different approach than we would have – why? Is it in reaction to a change in the domain so that more optimal paths to a solution are available? Did new challenges arise? Is there simply a “different” way of doing things? The latter is an interesting case as it suggests that the tool’s approach is still valid and still produces acceptable results. But as users of the tool, it is an important data point since it likely reflects a trend in the domain that will come up as we interact with peers or other tools.

Our students may be less-prepared for life after graduation

Whether AI is used in education or not, children in school today will graduate into a world of AI tools. They will need to know how to interact with them. This in itself is multifaceted. There’s a question of how to phrase a question or task. In the terminology of AI tools, this is referred to as prompting. It is the specific phrasing used to achieve the best result, as well as managing what background information the AI model should use (or not use). (The latter refers to managing the AI’s attention mechanism and context window. The context window can be thought of as an AI’s working memory. The attention mechanism is a major contributor to what ends up in that working memory.) In many respects, interacting with an AI tool can be like interacting with a child – one with peculiar cognitive strengths and deficits. As AI models evolve, the strengths and deficits will change, but (as with dealing with people) knowing how to play to a model’s strengths while sidestepping its weaknesses and being aware when it goes astray will be an integral part of the student’s adult life.

Keep Your Eyes On The Prize

It’s easy to become overwhelmed by the rapid pace of change spurred by AI. But despite all the new tools and processes that come about, keeping the big picture in mind will help ground discussions and decision-making. Are our graduating students well-prepared for what comes next, whether it is participating in the workforce, going to college, or taking their other paths through society? Can they navigate a world that includes AI but also still do well in those areas not touched by AI? High school, in particular, is a time of discovery and maturity, a chrysalis where children become young adults. Are teachers helping students see their potential and opening doors where that potential can develop? Can our students participate in society – do they leave school valuing teamwork, a sense of civic duty, and community?

The greatest risk is perhaps not in not using AI. There are a great many reasons why AI tools should not be immediately adopted – cost, their constant change, and unclear harms that could be incurred. But we need to make such decisions consciously and deliberately. The great risk is not in not using AI, but in ignoring AI. When we do that, we turn our back on the change happening around us, and those we are responsible for will be ill-prepared for a rapidly changing future.

At the end of the day

The question of what happens if we don’t use AI is best answered by bubbling up to a higher-level question: Are we doing the best we can at whatever we are doing? In some cases, the answer will be that we can do a certain task just fine without AI tools and with negligible side-effects (such as missing out on the latest greatest iteration of an AI tool, which we will no doubt hear about in the lunch room). In some cases, being able to do things ourselves earns a badge of prestige – we don’t need the AI.

In other cases, we will find that the loss of efficiency, skill, or opportunity afforded by an AI tool means we haven’t done the best we can.

We can also ask: Are our graduating students well-prepared for what comes next? – whether it is participating in the workforce, going to college, or taking other paths through society? Can they navigate a world that includes AI but also still do well in those areas not touched by AI (or when AI tools are absent)? High school, in particular, is a time of discovery and maturity, a chrysalis where children become young adults. Are teachers helping students see their potential and opening doors where that potential can develop? Can our students participate in society – do they leave school valuing teamwork, a sense of civic duty, and community?

The greatest risk is perhaps not in not using AI. There are a great many reasons why AI tools should not be immediately adopted – cost, their constant change, and unclear harms that could be incurred. But we need to make such decisions consciously and deliberately. The great risk is not in not using AI, but in ignoring AI. When we do that, we turn our back on the change happening around us, and those we are responsible for will be ill-prepared for a rapidly changing future.

The key is identifying those cases where AI tools impart a true benefit. The novelty and pace of AI change mean that we cannot rely as much on external guidance, but it must be developed ourselves and participate in the ongoing AI discussion.

Encouraging some individuals to take the vanguard and supporting their exploration is one of the best ways organizations can balance the risks and rewards and, as an organization, gain competence in the changing field of AI tools.