Learning to Use AI, Not Just AI Products

5 reasons why teachers should learn to use AI rather than AI products

The number of AI platforms and products is exploding as AI tools become cheaper and more easily accessible. Just about every educator already has access to ChatGPT, Gemini, and other tools, so much of the focus in the education industry is creating interfaces through platforms that automate AI use and, ultimately, take the teacher out of the process.

This is a mistake.

Reliance on these programs will not lead to improved teaching and learning because they remove the one skill that educators need to teach: professional judgment. Five concepts underscore this argument.

1. Intelligence Is Abundant, Judgment Is Scarce

As artificial intelligence becomes cheaper and more accessible, the true differentiator in education and professional life is no longer access to information. The hallmark of effective teaching will be the ability to apply judgment to AI use.

What now matters most is how humans interpret, adapt, and apply AI intelligently. Learning how to use AI broadly, rather than how to navigate a specific platform, builds that capacity for judgment. It moves the focus from mechanical tool use to thoughtful, reflective, and purpose-driven application.

2. Beyond Button-Pushing

Product-based training offers efficiency and convenience, but it can limit understanding. These tools often hide the reasoning processes behind their outputs, leaving users to accept results at face value.

When educators learn only to operate platforms like MagicSchool or other turnkey AI products, they become consumers rather than creators.

In contrast, working with general-purpose AI systems such as ChatGPT and Perplexity invites exploration and critical inquiry. Educators must decide how to define goals, frame prompts, evaluate responses, and refine outputs according to their needs and the needs of their students.

Each of these actions exercises professional discernment, which is the primary skill that AI cannot replicate.

3. Context, Constraints, and Critical Thinking

AI literacy rooted in broad understanding and application equips teachers to apply technology thoughtfully within their own instructional contexts. However, platforms, through their automations, are based on the idea that teachers do not need, or cannot apply, critical thinking.

The principles of good judgment, i.e., context sensitivity, constraint awareness, and alignment to learning needs, specifically determine whether or not teachers can implement effective, research-based pedagogy.

For example, an educator fluent in AI fundamentals can adapt a prompt to different reading levels, adjust tone for diverse learners, or critique bias in a generated text. These are acts of interpretation, not automation. They reflect the understanding that technology serves learning goals, not the reverse.

4. Professional Agency and Ethical Accountability

A core value of general AI education is professional agency. Teachers are the experts in their students, and their professionalism and expertise must govern how tools are used.

Educators must be able to say not only what AI can do, but what it should do in light of ethical and developmental considerations.

Platform-specific training tends to encourage dependence on predesigned workflows and features. In contrast, understanding AI as a flexible collaborator allows teachers to remain accountable for outcomes and take ownership of their professional responsibilities.

5. Building Transferable Expertise

AI will continue to evolve rapidly. Products will rise and fall, features will change, and interfaces will shift. What endures is the conceptual and ethical foundation that enables educators to adapt.

Learning AI as a set of professional competencies creates transferable expertise that can move across platforms. It also fosters a mindset of experimentation and lifelong learning, which are professional responsibilities essential to modern education.

Conclusion: Teachers Need Professional Development, Not Product Training

When educators learn AI itself, they cultivate the capacity to question, interpret, and direct technology with purpose. When they learn only the tools, they risk surrendering that judgment to the toolmaker.

The future of education depends not on mastering products, but on mastering the thinking that makes technology meaningful. The goal is not to produce trained AI users, but critical thinkers who are passionate about student learning and best able to meet the needs of their students.

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11/10/2025

David Bowman, EdAINow
info@edainow.com
https://edainow.com