The idea is that AI remains a tool, not an independent subject with its own motive (Activity Theory and the Cultural-Historical Approach), and the skill of the user determines its outcome (this includes ethical issues and speculation about AI replacing humans).
the usefulness of AI-based systems depends heavily on how much the user knows. AI tools, like chatbots or data analysis systems, are designed to assist people, not replace human thinking. If a person understands the subject well and knows how to use the AI tool properly, they can get better, more accurate results. They know what to ask, how to check the answers, and how to apply the results correctly. On the other hand, if someone has little knowledge, they might ask the wrong questions, misunderstand the answers, or use the tool in the wrong way. In short, AI works best when it is used by someone who knows what they are doing. The more knowledge the user has, the more useful the AI becomes.
I largely agree—AI systems are powerful, but their effectiveness depends on the user's domain knowledge, intent, and ethical grounding. Whether in diagnostics, education, or analytics, AI amplifies the expertise of the user, not replaces it. This aligns with Activity Theory, where the tool (AI) gains value only through purposeful human interaction and context-aware use.
In our paper “Enhancing Student Engagement through Innovative Pedagogy”, we showed how AI tools were most effective when guided by informed instructors who could contextualize the technology. Likewise, in “Neurofusion” and “RegNet-based Diagnosis”, the clinical utility of AI outputs still relies on expert interpretation and responsible decision-making, reinforcing that AI is a knowledge amplifier, not a substitute for it
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