Are schools preparing students for AI's future?

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Highlights

- What the study essentially found is that there’s widespread fear about AI's impact on jobs, and a significant belief that our education system just isn't keeping pace.
- The study found that over half the public, fifty-six percent, and even nearly sixty percent of employers, agree with the prediction that AI could eliminate half of these roles within five years.
- This, for me, is a massive red flag and a huge opportunity all at once.
- It's about how AI is helping us hold the complexity, so we have capacity for creativity.
- The real value is not in what the machine produces, but in how the student responds.

Transcript

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If this episode makes you think, please let us know in the comments and support us by subscribing and leaving a review. Thank you. Today we are exploring a really eye-opening new study from King's College London, published in Educate magazine, with the rather stark title Only One in Five Say Education is Preparing Young People for an AI driven future. It's a major piece of research, coming from the King's Institute for Artificial Intelligence and the Policy Institute. And it really dives deep into how the public, young people, university students and even employers are feeling about AI and the future of work. What the study essentially found is that there's widespread fear about AI's impact on jobs and a significant belief that our education system just isn't keeping pace. Now that headline statistic, only one in five or twenty percent of people believe our schools, colleges and universities are doing a good job of preparing young people for what's coming. Well that's a tough pill to swallow, isn't it? It suggests a real disconnect, a gap between the reality of AI's development and how ready we feel our students are. The report paints a picture of quite a lot of anxiety out there. Seven in ten people in the UK are worried about the economic impacts of AI, and a majority, six in ten, actually think AI will eliminate more jobs than it creates. Half even believe its impact will be worse than a normal recession. That's a powerful undercurrent of concern that we as educators need to acknowledge. What really caught my attention was the specific fear around entry-level white-collar jobs. The study found that over half the public, 56% and even nearly 60% of employers, agree with the prediction that AI could eliminate half of these roles within five years. Think about that for a moment. This isn't just a general unease, it's a very specific near-term threat to the kinds of jobs many of our students, especially those in the middle 80% we often talk about, might aspire to or traditionally enter after leaving education. It raises urgent questions about how we're designing pathways for them right now. Are we teaching students not to outsmart machines, but to outthink them? Because machines can compute, they cannot wonder, they cannot care. Here's where it gets even more interesting for us in schools. The study revealed that university students are already embracing AI tools, with 77% using AI at least a few times a month, and over a quarter 27%, using it daily. They're using it to write and edit text, to gather and summarize information, and even to prepare for exams. So the tools are definitely in the hands of our learners. But here's the crucial point. Almost 9 out of 10 students who use AI for their studies, 89% have encountered problems with it, most commonly errors and made up sources, and alarmingly fewer than half, just 43%, say they usually or always check and verify AI output before using it. This for me is a massive red flag and a huge opportunity all at once. It's a clear indication that while students are using AI, they're not necessarily using it well or critically. It speaks directly to our core philosophy of human in the loop. If students aren't verifying outputs, they're essentially outsourcing their thinking, not just their doing. They're succumbing to what I call cognitive debt. We're not fostering true AI literacy, which isn't about memorizing tool features, it's about collaborative reasoning ability. It's about understanding AI's limitations, managing those conversations with precision, and having a reflective awareness of its influence. It's about teaching them to evaluate, to determine accuracy, to identify bias, and to transform the output. That's the edit framework in action. If we're not explicitly teaching this, then we're setting them up for challenges, not success. What about employers? The study shows a bit of a mixed bag. Nearly nine in ten employers, 86%, reported at least modest improvements in productivity when workers use AI, so they're seeing the efficiency gains. But interestingly, the expert insight from Professor Eleanor Simple, director of the King's Institute for Artificial Intelligence, highlighted that employers see creative thinking as the top benefit AI can offer ahead of productivity, but both the public and experts doubt that today's tools truly deliver on this. This reinforces our purpose over technology approach. The real value isn't just in automating tasks, it's in freeing up human capacity for creativity, judgment, and complex problem solving. It's about how AI is helping us hold the complexity, so we have capacity for creativity. So if only one in five people feel education is preparing young people and students are already using AI, but not verifying its outputs, what does this mean for us? This isn't just about adding an AI module to the curriculum. This is about an evolution, not a revolution, but an evolution that needs to happen now. We need to look at our assessment practices and ask, can AI complete this without the student's unique context, perspective, or judgment? This is our cognitive stretch principle. We need to design learning that cannot be faked because it demands depth, care, and imagination. This might mean shifting away from solely product-based assessments to include in process, looking at AI interaction logs, and performance, seeing students demonstrate their understanding live. For a year eight geography lesson, for instance, it's not enough for a student to use AI to summarize information about climate change. The process needs to involve critically evaluating the AI's sources, identifying potential biases in its framing, and then using that output as a springboard for their own unique analysis, perhaps linking it to local geographical features, or proposing a community action plan. The real value is not in what the machine produces but in how the student responds. For school leaders and department heads, this study is a call to action. We can't wait for perfect conditions. We need to act now. This means thinking about how we anchor AI to existing friction points in teacher workflows to give them back time, energy, and focus to connect with students. It means building AI literacy for our staff, not just our students. A department head plan in CPD might look at this data and think, how can I empower my teachers to teach critical AI use in their subject? How can we explore tools like Notebook LM or Chat GPT for education to reduce administrative burden? Not just for the sake of efficiency, but to free up teachers for deeper pedagogical work. The public's appetite for government guaranteed retraining and regulation tells us that there's a wider societal expectation for intervention and guidance. As educators, we're at the forefront of this. We need to equip our students not just with technical skills, but with the irreplaceable human qualities wonder, care, judgment, relationship, imagination, wisdom, and ethics. These are the domains that AI simply cannot touch, and they are the very things that will allow our young people to thrive in an AI driven future, rather than fear it. That's all for today. Thanks for listening.