Education & Workforce Transformation — Foreword

Technology CEO/CTO | Board Member, American Society for AI (ASFAI)

When people ask what AI means for their jobs, they usually start with a fear: "Will this replace me?" It is a reasonable question. But the more important one is, "How can this technology amplify what I do best as a human?"

Used well, AI can help workers at every stage of their careers learn faster, solve harder problems, and create more value not by substituting for their judgment, but by extending it. More so than any technology that's come before it, AI truly has the potential to unlock new levels of human potential, but how do we achieve this while mitigating the risks?

But as some of the research in this section makes clear, that outcome is not guaranteed. In practice, the impact of AI depends not just on access to the technology, but on how work itself is structured around it. Emerging evidence shows that in some tasks AI can significantly improve performance, while in others it can degrade performance, especially when human judgment is over-relied on or under-exercised. This makes the question of how people collaborate with AI just as important as whether they use it at all.

Over the past decade, across work on Human + Machine and Radically Human, one pattern has stood out clearly: organizations that use AI to reimagine how work is done and people use AI outperform those that treat it as a pure automation play. They invest in new skills, new roles, and new ways of working that combine human creativity and empathy with machine speed and scale. They recognize that the real competitive advantage comes from people who know how to use AI as a tool, not from trying to entirely remove people from the loop.

That is why the Education and Workforce Transformation section of AI for Humanity is so important right now. Education is where societies decide whether AI will be something done to people or done with and for them. It is where we choose whether workers see AI as an inevitable threat or as a powerful new instrument they can learn to play. The chapters in this part of the anthology tackle that choice directly, from K–12 to higher education to lifelong learning and workforce development.

The contributors in this part start from a simple but profound premise: if AI is going to reshape work, then we have to reshape how people prepare for work. That means equipping learners not just with technical skills, but with the literacies, mindsets, and support systems they need to use AI responsibly and confidently. It means treating every classroom, training program, and workplace as a place where people can practice being "human plus machine" workers able to ask better questions, interpret AI outputs critically, and bring their own judgment and values to the table.

Across these chapters, you will see educators, technologists, and leaders wrestling with very practical questions. How do we design learning experiences where students use AI as a collaborator rather than a shortcut? How do we support teachers so they are not overwhelmed by "one more tool," but instead feel empowered to redesign their courses around higher value human work? How do we build pathways for workers especially those in roles most exposed to automation to reskill into new, AI enabled opportunities, and what role does business play in this journey? These are not abstract debates. They are the day to day design challenges facing schools, universities, companies, and governments.

What encourages me about this section is that it is grounded in real constraints and real hope. The authors are honest about the risks: biases in models that can reinforce inequities, surveillance style uses of AI in education that can erode trust, and the danger of widening the gap between those who have access to AI enhanced learning and those who do not. At the same time, they show concrete ways to turn AI into an engine of inclusion through accessible tools, culturally responsive content, and programs that bring AI literacy to communities that have historically been left out of technological revolutions.

You will also notice a common thread around agency. Again and again, the chapters return to the idea that learners and workers should not be passive recipients of AI systems. They should be co designers, testers, and critics. They should understand enough about how AI works to ask, "What data is this based on? Whose values are encoded here? Does this recommendation make sense in my context?" That kind of empowered engagement is only possible if education systems treat AI literacy as a core competency, alongside reading, writing, and numeracy.

AI for Humanity does something else that resonates with me: it models the kind of human AI collaboration it describes. The anthology and its interactive platform were built by people using AI to accelerate research, connect ideas, and create new forms of learning content. Humans made the key editorial and creative decisions; AI helped them experiment, prototype, and scale. That is exactly the pattern we see in leading organizations: people in the driver's seat, using AI to extend their reach rather than surrendering control.

If you are a teacher, leader, policymaker, or worker yourself, this section offers both a warning and an invitation. The warning is that if we do not redesign education around AI, we will deepen existing inequalities and leave millions of people unprepared for the new ways work is changing. The invitation is that we can instead choose to use AI itself to help solve this problem, building learning systems that are more personalized, and give people the skills, confidence, and support they need to thrive in a human plus machine world. That choice is still very much in our hands.

Taken together, the chapters by Aaron Poynton, François Candelon, Nathan R. Hill, Manuj Aggarwal, Robin Patra, Faith Bradley, Nicholas Harauz, Willonius Hatcher, along with Matthew Guggemos and Nicola Ianeselli offer a grounded and forward-looking view of AI in education and workforce transformation, from new-collar skills and enterprise readiness to reskilling policy, human–AI collaboration, digital decolonization, and cross-sector partnerships.

If we commit to education that treats AI as an amplifier of human potential, not a replacement for it, workers do not have to meet this moment with anxiety, anger or resignation; they can approach it with confidence, curiosity, and a renewed sense of opportunity. After all, as we wrote in Human + Machine, AI is a 'participant' sport, not a 'spectator' sport, and all of us must immerse ourselves and learn with that in mind. That is the spirit of the Education and Workforce Transformation section of AI for Humanity, and it is the mindset that will determine whether AI becomes a tool for shared prosperity or a source of deeper divides.