Disrupted History

Last week, in Part 1 of this reflection, I began with a memory: sitting at a red light near Perry Park in Brisbane with my young daughters, singing along to Miley Cyrus’ The Climb. That song played recently as I was working on some assessment. As I listened again to the lyrics, I reflected on the concern that has been pressing on many of K-12 teachers: How do we respond to the challenge of (some) students bypassing the cognitive friction through which learning is formed through their use of AI?

That post ended with a simple proposition. Central to our response must be teaching students to…

Think First.

In other words to use HI – Human Intelligence — before seeking support from AI.

We need find ways to teach kids ways to use their Brain first and AI second.

I now envisage that post as the first in a series of posts.

I’m going to serialise the follow up posts to it. They are going to come out on Wednesdays over coming weeks.

Each post will reflect on one aspect of the big question of …

How might K-12 teachers respond to the challenge of AI allowing students to conveniently bypass the cognitive friction through which learning is formed?

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It’s interesting to me how much of Bill Walsh’s book explore the nature of responding to challenge from a teaching and – although the term didn’t exists then – positive psychology. In fact, a section within part 1 of his book is literally called “The Top Priority Is Teaching”.

Like teachers demanding of their students to engage with the productive struggle of learning, Walsh was insisting that his organisation “dramatically lift the level of their thinking” (p.22). He pushed his charges to “demand and expect sacrifice from yourself” (p23).

These are ‘big asks’ of anyone – especially teenagers – who are presented with AI conveniently offering them the appearance of competence, relief from struggle, and speed without effort. Immediacte gratification.

In this post, I explore the connections I see between teaching for cognitive friction and some important ideas in positive psychology. The work of Seligman, of Dweck and of Duckworth.

Coming from a background of working for many years in the K-12 student wellbeing space, it’s hard to ignore these connections.

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Choosing Struggle to help students self-actualise the real goals of education?

My first post posited that, as we reimagine the role of teachers in an AI-infused world, we might begin to discover that our task is not to decide whether AI is good or bad. We might come to realise that our task, as teachers, might actually be to regulate the cognitive friction and to demand of our students that they ‘be more‘, ‘do more‘. Something richer and deeper. We should challenge them – and teach them to thrive, to flourish. That every day they engage in a ‘struggle’ to become their best versions of themselves – both as learners and as citizens. I think, although they may not be able to articulate it, that’s what kids and their parents actually really want from teachers and schools. That’s probably the real goal that underpins the History classroom. – maybe every classroom. We want our student to thrive, to flouish into the best versions of themselves.

Thriving and flourishing rest about a range of factors including finding purpose and meaning in our work.

I’d argue that, when helping our students to use AI effectively to enhance learning, we’re actually working to help them be better people. We’re help them to find meaning. To make sense of their world. To make better choices in their lives. The kind of choices that lead them to build a better world. This is why I’d argue that the study of History has a purpose that is generative and reparative, connected and community oriented.

Teachers have a special response in this journey. Teaching in an AI infused environment isn’t just about strategies and techniques. It’s certainly not about the technology in and of itself. It’s about helping kids grow to be great people.

As Bill Walsh put it:

The ability to help people around me self-actualise their goals underlines the single aspect of my abilities and the label that I value most – teacher. (Walsh, 2009, p.3)

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“The standard of performance”

This second part of that post now turns from the premise to practice. And this is where I want to lean more heavily on Bill Walsh – and then Dweck and Duckworth.

Walsh’s leadership philosophy in The Score Takes Care of Itself offers, I think, a useful way for schools to think about AI, assessment integrity, and learning culture. As one of the most coaches of the NFL, Walsh’s point was not (off course) that the score did not matter. (His world is, and was, very much a business of ‘getting’ results results. In his game – ‘a game of inches’ – there are metrics and data for everything!) Of course, the score in everyone of his 49ers’ matches mattered. He was coaching professional American football, not running some sort of utopian retreat on a hippie commune!

But Walsh understood that the scoreboard was not the work. The work was the Standard of Performance. The work was the culture. The work was the repeated habits, routines, expectations, feedback loops, discipline, and clarity that made high performance possible. The score was the visible result of thousands of less visible actions done well. He believed that leaders had to develop the right strategies for delivering success. This included developing the right planning for tackling various scenarios.

Flying by the seat of your pants precedes crashing by the seat of your pants… All personnel must recognise that your organisation is adaptive and dynamic in facing unstable ‘weather’. It is a state of mind. Situations and circumstances change so quickly… that no one can afford to get locked into one way of doing things… You bring on failure by reacting in an inappropriate manner to pressure or adversity… (Walsh, 2009, p-57-58)

Those ideas matters for schools in an AI-disrupted age.

If we focus only on the assessment product – the essay, the response, the multimodal presentation, the polished paragraph, the final mark – then we will keep chasing ‘the scoreboard’.

We will also keep being frustrated when students use AI to produce something that looks like learning but may not be learning at all.

So the task before us is not merely to police the scoreboard. It is to build a better standard of performance. A standard in which students understand that AI may support learning, but must not replace the thinking the task is designed to develop.

A standard in which the climb, the Mylie refers to, matters.

Here, then, is the first of eight (relatively) actionable ways that teachers might begin to build that standard in K-12 classrooms. Watch for posts on upcoming Wednesdays that will follow on with more tips.

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1 of 8: Teach the difference between productive struggle and wasted struggle

Explicitly.

Students (we?) need a shared language for difficulty. Many use AI because all difficulty feels like failure. That is understandable. Schooling can sometimes train students to see uncertainty as weakness and speed as competence. Add assessment pressure, adolescent self-doubt, comparison with peers, and the instant fluency of AI, and we should not be surprised when students reach for the easiest path.

So we need to teach the difference between productive struggle and wasted struggle… and I want to tie this concept of struggle to the work of Angela Duckworth – most famous for her studies of “grit”.

Productive struggle sounds a bit like:

“I don’t know yet, but I need to stick at this. Struggle is part of the process. Thinking this problem through – working at it, doing the grind – and all the frustrations I feel – will make me stronger. It’s how learning happens!”

Wasted struggle sounds like:

“I am stuck here. There’s actually no way through this at the moment. Attacking this problem on my own in this way if highly unlikely to be fruitful. There’s no reasonable or common-sense return on the effort I’m putting in here. I lack the key strategy, examples, feedback, vocabulary, prior knowledge, or access points to work this problem. Without them I feel; like I’m just spinning my wheels here!”

This distinction is important. It draws me to reflecting upon on the insights of Carol Dweck (Growth Mindset) and Angela Duckworth (Grit).

AI can be useful when students are caught in wasted struggle. It can break the problem open. It can clarify a term, provide an example, ask a guiding question, provide that ‘missing piece’, translate that inaccessible or complex language, or offer helpful feedback on a draft in ways no one else can. These sound like worthwhile use cases to me.

The stop the ‘bogged wheels spinning in the mud’ of learning. They reduce extraneous cognitive load. They provide ‘hope’ when problems seem unsurmountable.

Hope is one of the key predictors of wellbeing throughout our lifespan. As a result, it’s no surprise that poor mental health is often linked to loss of energy and fatigue due to a lack of hope… (A)nyone can become stuck in a bad situation. As a result, they may become tired of maintaining hope and believe that only something miraculous can help them transform their lives and create a flourishing future (Hope Theory: How Pathways Thinking Can Help Your Clients)

When a problem feels insurmountable in learning, students need support. At time, AI can offer that support to students. AI can Help Other Possibilities Emerge when student see none in the learning challenge they are facing. Sticking to working at a problem in the same way unsccessfully for too long isn’t a desirable challenge – it’s wasted effort! Sticky with trying to solve a problem with a strategy that doesn’t work isn’t ‘gritty’ – it’s often silly!

Duckworth argues that sticking stubbornly to a failing low-level goal isn’t authentic grit; it is a mistake. True grit requires being stubborn at the top, but incredibly flexible and willing to quit or pivot at the bottom. Sometimes it’s common sense and wise to offload to AI.

But AI becomes a problem when it removes productive struggle. When the task is asking the student’s brain to do the ‘hard work’ of learning – perhaps that’s retrieve, connect, evaluate, interpret, or judge, then AI should not do that work in the student’s place.

A possible classroom rule might be:

See struggle as desireable. Use AI when you are truly stuck not when the task is asking your brain to do the learning.

To be clear that is not some anti-AI stance. It is pro-learning. It is also a Walsh-like standard of performance. It names the behaviour we expect, not merely the result we want.

Students need to learn that effort is not always a sign that something has gone wrong. Sometimes effort is the work. Or, to borrow from Miley Cyrus, sometimes the uphill part is the point.

Use AI to help you on ‘the climb’. Not to replace ‘the climb’.

That’s where we, their teachers, must play a new roll.

Students, like many adults, may not be very good at identifying the distinction between the productive struggle of learning and wasted struggle. It’s just so tempting to replace the climb. AI’s convenience is so seductive.

As teachers, we are called to use our developed informed professional judgements to help students regulate their use of AI – yet every student’s struggle is different.

We will really need to KNOW our learners.

We need to be “deeply committed to learning and teaching” (Bill Walsh). We’ll need to ‘live up to the label of teacher’, to paraphrase Walsh.

We need to be both demanding and responsive in this role as regulators of learning. We need to support and model – and we need to demand ‘grittiness’ from our students. We must teach for students in a way that develops in them a growth mindset.

We should demand that our students to work hard and struggle to make sense of their world. To do the hard work to develop the skills to make it a better place. To…

Exhibit a ferocious and intelligently applied work ethic directed at continual improvement. (Bill Walsh)

and we should demand that of ourselves.

And that’s challenging in and of itself.