Technolandy

Day 62 (of 2025/26) December Book Review: The AI Experiment by Dr Stacie Chana— @staciechan Building With AI, Not Against It – #tEChursdAI early!

As someone who has long embraced emerging technologies—and who openly jokes about our future robot overlords—I approached The AI Experiment by Dr Stacie Chana with a curiosity about where this would push my thinking. What I found was not another tech-evangelist tract nor a fear-mongering warning, but rather a thoughtful and well-constructed guide to understanding, experimenting with, and collaborating with AI in educational contexts.

Purpose, Promise, and Delivery

The book’s purpose is clearly laid out: to invite educators into hands-on experimentation with AI, while grounding that work in ethics, pedagogy, and curiosity. Even as an early adopter myself, I found fresh insights, helpful frameworks, and well-crafted prompts that pushed my own thinking forward. Yet, the book never loses sight of the teacher who feels anxious about AI—those who might still quietly hope it gets banned. Instead, the text reframes AI as an opportunity rather than a threat.

This is, in my view, education’s “Blockbuster/Netflix moment”: change is coming—so will we cling to the old model, or pivot to build the new?

Distinct Contribution to the Field

In a time where AI is often talked about as a “cheating engine,” this book does something vitally different—it humanizes AI. It positions AI as collaborator, amplifier, and accelerator, not replacement. The book also makes a compelling argument that schools risk becoming relics if they fail to integrate AI as part of the real world students already inhabit.

Audience and Accessibility

The primary audience is clearly educators—and honestly, in 2025, that should mean all educators. The book scaffolds learning well: from foundational context to ethical framing to layered experiments. Teachers at every level—from AI-curious to AI-skeptical—will find points of entry and well-supported invitations to experiment.

The structure itself is a strength: each chapter cycles through explanation → discussion prompts → reflection → action steps. It loops key ideas in a way that encourages iterative learning rather than rigid instruction.

Next Steps: Where the Book Leads Us

The book doesn’t just show how AI works—it asks what we might do with it next. It acknowledges that AI is rapidly evolving (sometimes between draft and publication!), and that students and educators alike must approach the work as co-learners.

It also raises meaningful questions about equity, ethics, bias, creativity, and agency—moving us past simply “how do I use this?” toward “how do I use it well?”

Strengths of the Work

• Thoughtful progression from foundational concepts to ambitious, creative applications

• Excellent discussion and experiment prompts—very staff-meeting-ready

• A refreshingly honest treatment of AI as an evolving partner, not a magic box

• Embracing Piaget, Vygotsky, HITL (human-in-the-loop), centaur and cyborg metaphors

• A strong epilogue positioning AI as augmentation rather than automation

Importantly, the book’s tone is invitational, not prescriptive. It empowers educators to co-shape AI’s role in learning spaces rather than being passive recipients of technology.

Limitations (Which Are Actually the Point)

Some readers may wish for explicit recommendations—“Which tool should I use first?” But the book wisely resists that urge. Tools are changing by the month; it’s the why and how that matter. The book teaches mindset and methodology, not brand loyalty. There is no one-size-fits-all Alternative Intelligence to engage with!

Final Thoughts

The AI Experiment is not just a learning resource—it’s a provocation. It asks educators to reconsider what teaching is in an age of cognitive partnership with machines. It positions AI not as intellectual outsourcing, but as an accelerant for deeper thinking, creative production, access, and personalization.

For me, the book reinforced a belief I’ve long held: education must stop trying to wall itself off from the real world and instead become a living, evolving part of it. If we treat AI like calculators—once forbidden, now essential—we might finally free learners from mechanical tasks and let them engage in human ones: synthesizing, questioning, expressing, imagining. 

This book is a valuable companion for educators navigating this shift. It encourages experimentation with intention, collaboration with openness, and curiosity without fear.

And if our future robot overlords ever read it, I think they’ll appreciate that we tried to collaborate rather than resist.