The spread of artificial intelligence has raised a basic question for businesses, educators and policymakers: who is most likely to welcome AI into everyday life? A common assumption is that people who understand the technology best will be the most ready to use it.
Research published in Journal of Marketing points in the opposite direction. Across groups, settings and countries, people with less knowledge about AI were often more open to adopting it. The researchers describe this as the “lower literacy-higher receptivity” link.
What the research found
The pattern appears in more than one setting. An analysis of data from market research company Ipsos spanning 27 countries found that people in nations with lower average AI literacy were more receptive toward AI adoption than people in nations with higher literacy.
A survey of US undergraduate students showed a similar result. Students with less understanding of AI were more likely to say they would use it for tasks such as academic assignments.
That does not mean lower knowledge always leads to more trust in every situation. The research describes a more specific relationship: people who know less about AI may be more drawn to it when the technology seems to cross into areas that feel deeply human.
The role of “magicalness”
The explanation offered by the research is not that less-informed users think AI is technically stronger. Instead, the key idea is “magicalness.” When AI creates art, writes a heartfelt response, or plays a musical instrument, it can appear to do something that once seemed limited to people.
For users with less AI literacy, that effect can create awe. The technology may seem mysterious, surprising and almost human in what it produces. That sense of wonder can make people more willing to try AI tools.
People with more technical knowledge tend to see the machinery behind the output. They understand that AI works through algorithms, training data and computational models. That knowledge can make the same result feel less mysterious, even when it remains useful.
The source article is clear that AI does not actually have human qualities. A chatbot may produce an empathetic response, but it does not feel empathy. For people who understand that distinction more clearly, the humanlike surface of AI may be less powerful as a reason to adopt it.
Where the pattern is strongest
The lower literacy-higher receptivity link is strongest in areas associated with human traits. The research points to uses such as emotional support or counseling, where AI can seem to enter territory normally connected with care, feeling or personal understanding.
In these cases, receptivity appears to be tied to how extraordinary the technology feels. If an AI tool seems to produce something that resembles human expression, people with less knowledge may be more open to using it, even when they also have concerns.
The pattern changes for tasks that do not create the same humanlike impression. When the task is analyzing test results, for example, people with higher AI literacy are more receptive. In that setting, they may focus less on wonder and more on efficiency.
This difference matters because AI is not a single kind of product in the public mind. The same technology can be viewed very differently depending on whether it is writing, creating, supporting, analyzing or assisting. People may welcome one use while questioning another.
It is not just fear, ethics or capability
One striking part of the research is that people with lower AI literacy can still see AI as less capable, less ethical and somewhat scary. Yet their openness can remain higher. That suggests their receptivity is not simply based on confidence in the technology.
Instead, the draw comes from what AI seems able to do. A person may find AI unsettling and still be intrigued by its ability to perform a task that feels creative, emotional or human. The sense of wonder can coexist with doubt.
This helps explain why public reactions to emerging technologies can be so mixed. Some research describes “algorithm appreciation,” where consumers favor new technology. Other work describes “algorithm aversion,” where people resist algorithmic systems. The research summarized here adds another factor: whether AI feels magical to the person encountering it.
The challenge for AI literacy
The findings create a difficult balance for people trying to improve AI literacy. Teaching people how AI works is important because users need to understand both benefits and risks. But making the technology more understandable may also reduce some of the awe that helps drive adoption.
That does not mean educators or policymakers should preserve confusion. It means they need to recognize that knowledge changes perception. When AI becomes less mysterious, people may evaluate it in a more practical way, especially by looking at whether it is efficient, useful or appropriate for a given task.
For businesses, educators and policymakers, the takeaway is practical. AI products and services should be developed and deployed with public perception in mind. People are not only judging AI by its actual performance; they are also responding to how humanlike, mysterious or awe inspiring it appears.
The best outcome described by the research is a balance: help people understand AI’s benefits and risks without eliminating the sense of awe that makes many people willing to engage with it in the first place.