For example, some of us were there in 1996 when IBM's program Deep Blue beat chess grandmaster Garry Kasparov.

Some of you remember that?

In 1998, when Google launched its search engine.

Most of this audience was there in 2011, when Watson the computer won Jeopardy and Siri became a pocket virtual assistant.

And we definitely all were there in 2023, when ChatGPT passed the bar exam in the top 10%.

You may have realized that these dates are all milestones associated with AI, artificial intelligence, the ability of machines to learn from experience.

Over that time, AI and machine learning have disrupted just about every industry, including my own, education.

Teachers, students, and parents are wondering, what is learning when software can answer every question?

Winston Churchill said, never let a good crisis go to waste.

Teachers would call this a learning moment.

I see it as an ideal opportunity for us to reimagine the future of education.

Unfortunately, my industry's response to change is often flight or fight.

When a new disruptor becomes overwhelming, like students with cell phones in the classroom, we're tempted to throw in the towel and say, I'm just going to teach the way I always have.

That's flight.

Or we try to fight.

We're told to combat artificial intelligence by using software that promises to detect AI-generated products.

But technology evolves way too fast for any technical solution to keep pace.

Teachers are also advised to monitor their students more closely to prevent high-tech cheating.

Big brother and big sister watching over every student's shoulder every minute is not only impossible, it goes against our mission of building trust, responsibility, independence, and a passion for learning.

There are other reasons fight or flight won't work.

There's flight.

There's fight.

For one, AI is everywhere.

AI-powered apps are making resources increasingly more accessible, removing barriers like cost and language.

And overall, AI has improved efficiency and productivity, which obviously doesn't count the hours we use recording AI-enhanced TikToks.

And AI is growing.

In 2022, another milestone year, Forbes reported 2 million images generated in DALI each day. 145 million Americans, 45% of us, use a voice assistant on a regular basis on over 4 billion devices and the global AI market reached $140 billion, expected to grow by 20% per year.

So it's pretty clear AI technology is popular, it works, and it's making money, all of which means it's not going anywhere soon.

So what's the problem?

Well, we probably shouldn't ignore issues with privacy, intellectual property rights, accuracy, bias, even propaganda, and the difficulty of regulating these technologies.

On top of these problems, as AI gets better at generating all kinds of output, educators and parents are concerned with issues of academic integrity.

We're worrying it could cause students to miss important context, amplify prejudices and misconceptions, even bypass the learning process altogether.

Now, I was there to experience when many in my profession feared how thesauruses, calculators, video, and spellcheck might ruin our students' abilities to read, write, and do math.

Today, obviously, we find these in just about every classroom.

In that spirit, some educators have discovered a third choice to flight or fight, adapt.

They're making artificial intelligence their co-teacher by using it to brainstorm lessons, evaluate assignments, even serve as a 24-7 student help desk and fact checker.

Others are teaching students about AI technology so they can use its benefits more productively and ethically, understand its limitations, and even consider it as a career.

Now, these steps may go a long way towards making AI work in our classrooms, but they don't address the fundamental question of how we measure learning when machines and devices can write essays, generate charts and images, code software, and ace tests.

I suggest that the real threat to our children's education is not machine learning and artificial intelligence.

It's how we've come to measure and value human learning and human intelligence.

A basic definition of learning is the process of gaining understanding through study and experience.

Since the 1950s, computer programmers have been developing algorithms that train machines to process information and make decisions that improve with each success or failure.

Now, that should sound familiar because machine learning is modeled on the human brain.

Intelligence has been defined as the ability to acquire and apply that knowledge and skills.

We say a program is artificially intelligent when it can use the information it's received and learned to answer customized inquiries like, what's the best Bluetooth speaker to buy or write me a heartfelt speech for my friend's wedding.

The problem with these traditional definitions of learning and intelligence is that they excel at.

Machines can beat chess masters, win games of trivia, even predict power failures better than human experts because for these kinds of tasks, they can access and analyze more information far faster than we can.

These definitions also don't reflect the neurodiversity of our students, each of whom thinks differently and brings different understandings and experiences to our classrooms.

And when we assign too high a value to the accumulation of knowledge, the memorization of procedures, and the recall of facts, it's no wonder students turn to technology for answers.

I propose that we redefine and revalue what we consider intelligence.

The good news is the means to that end are probably already in our teacher toolkit.

They're just buried in there somewhere under a pile of Scantron forms.

Let's design more lessons and ask more questions with these five characteristics.

First, emphasize live hands-on experiences.

Then ask, what did you notice when this happened?

How might your actions have affected the outcome?

How did your team collaborate on this task?

Problem solving through discovery, exploration, and reflection is part of what we call active learning, which we know to be more effective than old school models like lectures, worksheets, and book reports.

Second, ask questions about meaning.

Encourage students to identify their beliefs.

See the bigger picture, generalize concepts from one area to the other.

Make learning personal by asking, what does this information mean to you?

What might it mean to our community or another community?

Why is this relevant to anyone?

And when children ask us, why do I need to learn this?

Adults should have a good answer.

Third, ask students to make connections to the real world, to integrate the pieces of what they know.

Studies show students are more engaged when they understand context.

For example, how does this relate to last week's lesson or to what you're learning in social studies or art class?

How do your personal, local, and global perspectives compare?

How might this give you insight on the past, present, and future?

Fourth, ask students about their feelings.

Imagine that.

Awareness, empathy, putting yourself in someone else's shoes, and playing nice with others are what we call emotional intelligence.

As much as we're putting AI into devices that are always listening, maybe even watching, AI still can't read the room.

A one-year-old child, even your cat or dog, can sense your mood better than any computer.

Now, we don't normally ask students to flex these four muscles, so things may not go smoothly at first.

But let's keep in mind, struggle is a necessary prerequisite for learning.

Finally, let's have students direct their knowledge and skills towards novel applications.

Ask them to apply their current understandings to a new situation or create a new way of looking at an old problem.

Only humans can think divergently like that, outside the box.

Computers are the box.

Artificial intelligence can only follow the rules they've been given.

AI can't experience, generalize, reason, reflect, think abstractly, or understand relevance.

And it can't access a student's perceptions, interpretations, and feelings.

A good education should do all of these things.

This is how we future-proof our classrooms.

Make AI a co-teacher, work with the technology's strengths and weaknesses, but most importantly, let's value human intelligence as more than just computer-like processing.

Get students to use parts of their brains computers don't have.

Ask questions AI can't answer.

Devise problems chatbots can't solve.

We can do that by challenging students with authentic experiences that tap into the incredible capacity, diversity, and creativity of their human minds.

Now, I can picture the day when I'll be able to say to my future self, imagined here, ironically, by artificial intelligence, that we were all there, and used this learning moment to influence the evolution of education for the better.

Then, we can all confidently say to our students, we helped you find your there, wherever and whenever it may be.

Thank you.