I have been reflecting on the work we did, and adults are once again being asked to guide young people through a technology they are still learning to understand themselves. Looking back over the years I spent researching how internet technologies could be used constructively in schools, I have been struck by how many of the challenges we encountered then are reappearing today in a different form. The technologies have changed. Human nature has not.
During those years, we worked together with students and teachers, finding ways to enhance the students’ learning with the help of internet technologies and multimedia systems. I don’t really want to write about what we did, but rather about a few of the truths we discovered concerning who benefited from participating in our projects, and whether these findings might also be pertinent to the current situation of AI in schools.
I’d like to address three main learnings.
The first is that the students who benefited, meaning those who showed an increase in their engagement and confidence, were not necessarily those in the top percentile. Those students performed well and showed only a marginal increase in engagement compared with when they were having standard classes. Neither did the students in the bottom percentile show a significant increase in terms of performance. In both cases, the results were probably due to the novelty of using technology in creative ways to learn topics in their curriculum.
No, the students who benefited the most were those hovering slightly above and below the average. We researched why this was and came up with various theories. The one that struck me most had to do with the allocation of responsibility and the transparency of outputs.
When we started a project, the class would be separated into various teams. In each team, there were those who had to document the progress of the project, those who helped create the end results, and one or two who were responsible for the technology. The latter were also responsible for teaching the other students how to use the hardware and software properly.
I had a few university IT students who came with me into the classes, and we taught the “techies” and gave them clear guidelines about how to care for the devices. Then we saw to it that they were able to help their team members use the devices or software properly.
We would nearly always choose those students as our techies who were underperforming, had high absenteeism, or were somewhat introverted. Not the troublemakers, but those who were at risk of falling behind. We did not elevate them to the top of the castle; rather, we made them realise that the other students, and we in the research party, needed their support. They were the ones the other students called if they were stuck with a problem. They were also responsible for doing backups of data and collecting all the devices and handing them over to us after every work session.
In the months we worked with them, it was possible to see them grow, both outwardly and inwardly. The data we collected only confirmed what we saw happening before our eyes. In two cases, teachers told me how students who had been close to dropping out decided to finish school and go on to university because, as they put it, they didn’t know learning could be fun.
Looking back, I think this has implications for how we introduce AI into classrooms. When students were given meaningful responsibility within a team, particularly responsibility that other students depended upon, many who were quietly drifting through school began to flourish. Responsibility transformed identity in ways that grades alone rarely could. AI may become another opportunity to rethink not simply what students learn, but the roles they are invited to play while learning.
The second aspect I noticed was that students, of any age, were not particularly concerned when things went wrong with the technology, and they were patient in learning how to use a piece of software or hardware so that they got their desired results. In one Grade 6 project, where children were learning about metamorphosis (biology) by creating stop-motion animation films (art), they patiently took twenty shots for each second of their five-minute movie. There was one poor student who had to sit motionless against the trunk of a tree while he metamorphosed into a mound of dirt, moss, and twigs.
Throughout the span of those projects, over all those years, we sometimes heard, “This is hard,” but we never heard, “This is too hard.” Over and over again, I would observe some techie explain how some software worked, amazed at how the other students didn’t balk at the lengthy explanation and endless, “Click here. Then here. Then here. Then here...” Unfortunately, this could not be said of most of the teachers we worked with.
Children often approach unfamiliar technology with curiosity before judgement. They expect to experiment, make mistakes, and gradually improve. That doesn’t mean they should unquestioningly trust everything technology produces. Quite the opposite. It means they are usually willing to persist long enough to discover both its possibilities and its limitations. That willingness to explore without expecting immediate mastery may be one of the most valuable attitudes we can preserve as AI enters education.
Which brings me to the third thing I observed over those years of doing research. The greatest challenge, however, wasn’t technical. It was psychological.
In many instances, it was the teachers who struggled most with learning the technology, not the students. This was at a time when the use of internet technology in schools was only just beginning. When we collected data on the teachers, very few used internet technologies either privately or professionally.
When we approached the teachers about doing projects, they were often curious and open-minded. They were excited for their students. It took them a moment when we told them the students would be teaching each other how to use the technology rather than being taught by the teachers. We always had one-on-one sessions with the teachers as well, which often took much longer than it did for the students to learn.
In my experience, this had to do with two automatic responses that would trip them up. These were, “I’ll never be able to remember this,” when we were giving step-by-step instructions, and, “Why is it so complicated?” when outlining how the students would integrate the technology. This discomfort and internal resistance was prevalent among nearly all the teachers we worked with, regardless of age or the subjects they taught.
In essence, we were devising a situation where students, the ultimate learners, were having to teach each other how to use the technology. At the same time, we were putting teachers, masters at teaching, in the precarious position of being learners.
The shift from teacher to learner was disconcerting. Whereas the students dealt with software glitches or technical failures as part of the learning process, the teachers often felt they were doing something wrong or that the technology itself was inadequate whenever errors occurred.
Our job was often helping teachers see that fixing what goes wrong was part of learning. Instead of expecting everything to work all the time perfectly, we asked them whether they could see technical problems as opportunities for problem-solving. The moment they noticed how unperturbed their students were when things went wrong, and that the students didn’t expect their teacher to fix everything, the teachers themselves began learning how to learn in a way they had not anticipated.
This is not to say the students became the teachers. Rather, by observing their students, the teachers were able to turn inward and reflect upon their own ability—or inability—to deal with uncertainty. This often came with the realisation that much of their professional energy was focused on becoming better teachers, but far less on remaining active learners themselves.
I don’t believe the teachers struggled because they lacked ability. They struggled because they cared. They were accustomed to standing in front of a classroom feeling responsible for having answers. Suddenly they found themselves learning in public alongside their students. That is an uncomfortable position for anyone, regardless of experience.
Generative AI introduces questions our multimedia projects never faced: hallucinated answers, bias, authorship, privacy, and the temptation to substitute generated responses for genuine understanding. Those issues deserve careful attention. Yet they do not remove the central challenge we observed all those years ago. They reinforce it. Adults will once again have to model what it means to be thoughtful learners while guiding young people through unfamiliar territory.
This is a long detour to arrive at the point I want to make. When it comes to AI, leaders, teachers, and parents cannot wait until they feel completely confident before inviting young people to engage with it. Learning and teaching will have to happen simultaneously. We do not have the luxury of figuring everything out and fixing every glitch before students encounter the technology. What we can do is cultivate curiosity, critical thinking, and a willingness to experiment while remaining rigorous in evaluating what AI can and cannot do.
We know AI will change the landscape of learning in the future. What is discomforting to many is that the future is now.
Photo by Markus Spiske on Unsplash
Last week, I sent the manuscript of the first twenty-two of twenty-seven chapters of my memoir to an editor. It is what I hope will prove to be a good first draft. The editor is someone I trust to do a thorough line edit as well as provide creative feedback.
What surprised me most was not that I had finished twenty-two chapters. It was that this was probably the strongest first draft I have written in all my years as a content developer. Granted, writing a memoir is another kettle of fish, and I still have to hear back from the editor; they might see it otherwise.
What I am anxious to find out is whether working with an AI writing coach has truly paid off in this initial phase, as far as the quality of output. What is indisputable is that the experience of using an agent changed how I approached my writing. I had more time to write and less time to brood.
After reading Dr. Philippa Hartman’s article, The “Cognitive Offloading” Paradox, I was fascinated to discover that many of the ways I had instinctively been working with AI closely matched what she describes as “committed, strategic offloading,” a predictor of positive, even transformative learning.
Hartman discusses six principles to follow.
Principle 1: Offload to AI substantially, or not at all.
When it comes to writing, up until now, I have used ChatGPT, together with Grammarly and Claude, to edit my spelling and grammar.
I tried, unsuccessfully, to give ChatGPT more complicated editing tasks to do, but found this dissatisfying. This is partially because the AI tools introduced certain language patterns I don’t like at all, such as em dashes, one-sentence paragraphs, and an overuse of adjectives. As well, at the Pro project level, ChatGPT only seemed able to process tasks involving documents up to ten pages long. If, for instance, I asked it to remove the time lines and clean up the text of a podcast transcript longer than fifteen pages, the results were suboptimal: missing sections, cut-off sentences, and hallucinations. Any discussion with ChatGPT about how to alter my prompts to achieve consistently reliable results failed.
Admittedly, the problem might have been on my side, but I didn’t want to spend more time trying to get the model to produce better outcomes. I also didn’t want to run the risk of my book reading as though AI had stomped all over it, leaving the language flat and the imagery matted. I found two friends with the required expertise to do the editing. Human writing for human readers.
Having dyslexia meant I still relied on Grammarly as a spell checker.
The one area I was excited to work with AI was as a writing coach. This is the first time I have written a book, and I needed guidance. After taking numerous online courses in creative nonfiction writing, I felt more confused than motivated. I needed a writing coach to help me with structure and form. Fortunately, a good friend worked with me and programmed an AI agent as a writing coach.
Principle 2: Frame AI as a partner, not a tool.
My writing coach was not so much a partner as an informed expert, possessing a skill set I needed. I’ve probably read dozens and dozens of memoirs in my life, some of them among my favourites, but I knew nothing about what makes a memoir engaging and appealing to today’s readers. For this, I needed feedback from someone or something that could review my work from a publisher’s perspective, and also had the ability to support my writing process without creative intrusion.
What I was seeking was a coach who would listen to my questions and answer them with specificity. I was also hoping the coach could help me distance myself from what I was writing, so that I could make changes out of conviction that they would add cohesiveness and clarity, rather than out of desperation or insecurity.
The final selling point was the ability to converse with this coach about anything that came to mind, and at times convenient for me. I didn’t need to feel beholden to this coach, nor was I concerned about testing the boundaries of the relationship. This freedom of when, where, and what of our so-called partnership was joyful.
Principle 3: Build verification into the workflow, not the preamble.
One of the core tasks I assigned the coach to do was to analyse each chapter for what was working, what needed improvement, and what was missing. The book has four parts with varying numbers of chapters, so I asked the agent to make sure each chapter supported the central theme of its respective part and that each part was identifiable from the others.
The coach would write an analysis of what worked and what didn’t in the version I submitted of each chapter. As I read through the response, I registered whether I “liked” what was being said or not. Everything I liked went into a “maybe true” bucket. I didn’t feel I could necessarily trust the positive comments as being true, but neither could I dismiss them. I gave myself permission to let them stand for the moment. It was reassuring to hear these comments and soothed my doubts, which enabled me to continue writing with momentum.
Then there was the feedback I didn’t like reading. This tended to fall into two other buckets. The first was the “whatever” bucket. These were nice-to-have suggestions, but not for this round of revisions. Advice about how I could build an even higher sandcastle. Things that would take too much time and not substantially improve the current version.
There were also comments so unhelpful that I questioned the feasibility of continuing to use the AI coach. I treated these as anomalies and discarded them almost immediately.
Then there were comments or ideas I didn’t like or were resistant to, but that provoked a response that made me want to explore them further. One suggestion was that I move an entire chapter to a different part of the book. My immediate reaction was, “Absolutely not.” Two days later, I realised the suggestion exposed a weakness in the structure, even though I ultimately solved the problem differently. Those were the conversations that taught me the most.
If a suggestion made me pause, I’d ask the agent to give me some examples and ask follow-up questions. What would be an alternative to what I’m doing now? Why is this suggestion useful? What will it improve? Then a conversation would ensue. Not between adversaries, but between two parties working toward a common outcome. I was well aware that the AI agent was not a committed partner, but the thread of discussion often felt as though it was.
Principle 4: Make the learner think first, AI second.
In the creative process of writing a book, I am the learner, so it is up to me to do the work, both the heavy lifting and the light touches. Using the coach reminds me of something my father used to say: “The best thing about buying your first car is paying for it.” As someone who has never owned a car, I do not have direct experience with this, yet I do know the satisfaction of taking a piece of advice from the agent, rewriting part of a chapter or expanding on an idea, and recognising that the piece is now better.
The coach could make suggestions endlessly, but until I wrestled with those suggestions myself, nothing had really been learned. The learning happened in the rewriting, not in reading the feedback. Every decision remained mine. Sometimes I accepted a suggestion wholeheartedly. Sometimes I rejected it immediately. More often, one suggestion led me to an entirely different solution that neither the coach nor I had imagined at the outset.
All learning begins with baby steps. Sometimes I felt very wobbly on my legs, the way babies learning to walk resemble drunken sailors. I would take a group of suggestions and begin revising, only to abandon the whole approach halfway through. Since I was using Scrivener as my writing platform, wobbling back to the starting line was no problem.
I never tracked changes. Instead, I rewrote the text, paused for a day or two, and then read the chapter again. I asked myself whether it now flowed smoothly and whether the ideas followed one another more naturally. If not, I returned to the previous version and tried another approach. If it felt right, I moved on to the next chapter and let the revised one rest for a while before returning to it once again with fresh eyes.
That ownership turned out to be essential. The coach could accelerate my thinking, but it could never replace it.
Principle 5: Use AI to identify errors, not fix them.
I love this use of the AI agent most of all. The best feedback I receive is when I ask, “What improvements or changes should I make? What is missing?” Especially the latter question exposed obvious oversights or prodded tender blind spots. Since the information came from a machine, nothing it said felt personal. I only had to ask myself whether what it said made sense or not. Nothing more.
There were two aspects of identifying errors where my AI coach fell short. One was that it had no concept of “good enough.” If I asked it whether the current version of a chapter was good enough as a first draft, whether it had reached a comparable level to the other chapters, the answer was always yes-and-no. There were always things that still needed fixing.
The other shortcoming was that no text was considered beyond saving. No matter how weak or incomprehensible a piece was, it apparently merited practical suggestions for how to improve it. Of course, the coach never said it was a mess.
It happened twice that I worked and worked on a chapter, allowing the AI agent to lead me down the garden path, only to realise that all my efforts were useless. I had to throw the chapters away and start again. Somehow, I wasn’t willing to give up as long as the agent kept making suggestions. This was a valuable lesson to learn. Like the Monty Python sketch about the Black Plague, my text was piled onto the wagon headed for a mass burial, and the agent was singing, “Not dead yet!”
Principle 6: Assess without the scaffolding.
This is where I am now, waiting for my editor’s verdict. If the feedback is largely positive, I will send the manuscript to the second editor. I fully expect many changes, but I also hope they will confirm that the heart of the book is there.
If that happens, I will decide whether to continue using the model in the next round or whether it has already served its purpose.
If the feedback reveals more fundamental problems, I will have to examine not only the manuscript but also the way I used AI throughout the process. That may bruise my ego, but learning something new often does.
In the end, Hartman’s article may prove to be right. The real question was never whether AI could write my book. It couldn’t. The question was whether it could help me become a better writer. That is something only another human being, my editor, can now help me answer.
Title: Titus Groan, by Mervyn Peake
First time I read the book: when I was dancing full-time at Les Grand Ballet after returning from Cannes, France
I recently decided to download this book as an audiobook, and I am delighting in this book again. I remember being enthralled by the strangeness of the world he presents and how relatable his characters were. They were not nice people, nor were they intrinsically bad. They were, rather, just flawed.
