Tag Archives: curriculum

Looking at AI and the future of schools

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There is no doubt that Artificial Intelligence (AI) is going to influence the way we do school in the very near future. I have been pondering what that influence will look like. What are the implications now and what will they be in just a few short years.

Now: AI is going to get messy. Unlike when Google and Wikipedia came out and we were dealing with plagiarism issues, AI writing is not Google-able, and there are two key issues with this: First, you can create assignments that are not Google-able, but you are much more limited in what you can create that is un-AI-able. That is to say, you can ask a question that isn’t easily answerable by Google search, but AI is quite imaginative and creative and can postulate things that a Google search can’t answer, and then share a coherent response. The second issue is that AI detectors are not evidence of cheating. If I find the exact source that was plagiarized, it’s easy to say that a student copied it, but if a detector says that something is 90% likely to be written by AI that doesn’t mean that it’s only 10% likely to be written by a person. For example, I could write that last sentence in 3 different ways and an AI detector would come up with 3 different percentages of likeliness that it is AI. Same sentence, different percentage of likelihood to be AI written, and all written by me.

So we are entering a messy stage of students choosing to use AI to do the work for them, or to help them do the work, or even to discuss that topic and argue with them so that they can come up with their own, better responses. We can all agree that the three uses I shared above are progressively ‘better’ use of AI, but again, all are using AI in some way. The question is, are we going to try to police this, or try to teach appropriate use at the appropriate time? And even when we do this, what do we do when we suspect misuse, but can’t prove it? Do we give full marks and move on? Do we challenge the student? What’s the best approach?

So we are in an era where it is more and more challenging to figure out when a student is misusing AI and we are further challenged with the burden of proof. Do we now start only marking things we see students do in supervised environments? That seems less than ideal. The obvious choice is to be explicit about expectations and to teach good use of AI, and not pretend like we can continue on and expect students not to use it.

The near future: I find the possible direction of use of AI in schools quite exciting to consider. Watch this short video of Sal Hahn and his son, Imran, working with an Open AI tool to solve a Math question without the AI giving away the answer.

When I see something like this video, made almost 6 month ago, I wonder, what’s going to be possible in another couple years? How much will an AI ‘know’ about a student’s approach to learning, about their challenges? About how best to entice learning specifically for each student? And then what is the teacher’s role?

I’m not worried about teachers being redundant, on the contrary, I’m excited about what’s possible in this now era. When 80% of the class is getting exactly the instruction they need to progress to a grade standard in a class on the required content, how much time does a teacher having during class time to meet with and support the other 20% of students who struggle? When a large part part of the curriculum is covered by AI, meeting and challenging students at their ideal points of challenge, and not a whole class moving at the class targeted needs, how much ‘extra’ time is available to do some really interesting experiments or projects? What can be done to take ideas from a course across multiple disciplines and to teach students how to make real-world connections with the work they are studying?

Students generally spend between 5 and 6 hours a day in class at school. If we are ‘covering’ what we need to with AI assistance in less than 3 hours, what does the rest of the time at school look like? Student directed inquiries based on their passions and interests? Real world community connections? Authentic leadership opportunities? Challenges and competitions that force them to be imaginative and creative? The options seem both exciting and endless.

The path from ‘now’ to ‘the near future’ is going to be messy. That said, I’m quite excited about seeing how the journey unfolds. While it won’t be a smooth ride, it will definitely be one that is both a great adventure and one that is headed to a pretty fantastic destination.

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Update: Inspired by my podcast conversation with Dean Shareski, here.

Content Free Learning (in a world of AI)

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Yesterday, when I took a look at how it’s easier to make school work Google proof than it is to make school work AI proof, I said:

How do we bolster creativity and productivity with AND without the use of Artificial Intelligence?

This got me thinking about using AI effectively, and that led me to thinking about ‘content free’ learning. Before I go further, I’d like to define that term. By ‘content free’ I do NOT mean that there is no content. Rather, what I mean is learning regardless of content. That is to say, it doesn’t matter if it’s Math, English, Social Studies, Science, or any other subject, the learning is the same (or at least similar). So keeping with the Artificial Intelligence theme, here are some questions we can ask to promote creativity and productivity in any AI infused classroom or lesson:

“What questions should we ask ourselves before we ask AI?”
“What’s a better question to ask the AI?”
“How would you improve on this response?”
“What would your prompt be to create an image for this story?”
“How could we get to a more desired response faster?”
“What biases do you notice?”
“Who is our audience, and how do we let the AI know this?”
“How do we make these results more engaging for the audience?”
“If you had to argue against this AI, what are 3 points you or your partner would start with?”

In a Math class, solving a word problem, you could ask AI, “What are the ‘knowns and unknowns’ in the question?”

In a Social Studies class, looking at a historical event, you could ask AI, “What else was happening in the world during this event?” Or you could have it create narratives from different perspectives, before having a debate from the different perspectives.

In each of these cases, there can be discussion about the AI responses which are what students are developing and thinking about… and learning about. The subject matter can be vastly different but the students are asked to think metacognitively about the questions and tasks you give AI, or to do the same with the results an AI produces.

A great example of this is the Foundations of Inquiry courses we offer at Inquiry Hub. Student do projects on any topics of interest, and they are assessed on their learning regardless of the content.  See the chart of Curricular Competencies and Content in the course description. As described in the Goals and Rationale:

At its heart inquiry is a process of metacognition. The purpose of this course is to bring this metacognition to the forefront AS the learning and have students demonstrate their ability to identify the various forms of inquiry – across domains and disciplines and the stages of inquiry as they move through them, experience failure and stuckness at each level. Foundations of Inquiry 10 recognizes that competence in an area of study requires factual knowledge organized around conceptual frameworks to facilitate knowledge retrieval and application. Classroom activities are designed to develop understanding through in-depth study both within and outside the required curriculum.

This delves into the idea of learning and failure, which I’ve spoke a lot about before.In each of the examples above, we are asking students challenging questions. We are asking them to critically think about what we are asking AI; to think about how we can improve on AI responses; or, to think about how to use AI responses as a launching point to new questions and directions. The use of AI isn’t to ‘get to’ the answer but rather to get to a challenging place to stump students and force them to think critically about the questions and responses they get from AI.

And sometimes the activity will be too easy, other times too hard, but even those become learning opportunities… content free learning opportunities.

Lack of integration not information

We have access to more information than we could ever use. The sum of knowledge available to us is far beyond anyone’s comprehension. Creativity and ingenuity do not come from more knowledge but rather two kinds of integration:

1. Integration of understanding.

There is a difference between understanding how an ocean wave works, and knowing when to catch a wave when surfing or body surfing. There is a difference between studying covalent bonds and understanding how two chemicals will interact.

2. Integration of fields of study.

A mathematician who sees poetry in a series or pattern of numbers. An engineer who sees an ant nest and wonders what they can learn about airflow in buildings.

In this day and age, lack of information is seldom the problem, but lack of integration is.

For schools, integration means getting out of subject silos, and thinking about cross-curricular projects. STEM and STEAM education, and trying to solve hard problems without a single correct answer. Integration of curriculum, inquiry learning, iterations, and learning through failure by hitting roadblocks that require out-of-the-box thinking and solutions.

Integration comes from challenging experiences that require base knowledge in more than one field. So, while knowledge and information are necessary, information is not sufficient without integration of ideas from other subjects and fields. The learning really begins where subjects and concepts intersect… and where learning across different fields is meaningfully integrated.