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Of course, kids are a relatively easy audience for Khanmigo, as they are naturally open to such innovations. They don’t carry years of “learning fatigue,” forged by sitting through endless lectures and associating study time with boredom. AI meets them where they are, unspoiled and eager.
Now imagine a different scenario: a car equipped with AI that tracks your facial expressions and eyelid movements to detect when you’re too tired to drive safely. It suggests, perhaps with a subtle alarm, that you pull over for a rest. Tell that to my grandpa, though, and he’d probably chuckle at the idea that a camera could know better than he does when he needs a break. There will always be early adopters — those eager to embrace the new and exciting — and those who resist, for reasons that may be logical or deeply personal. For instance, some might worry that AI will take their job, while others may mistrust the technology purely because it feels unfamiliar or intrusive. Understanding and addressing these perspectives is the first step towards designing AI systems that can bridge the gap between skepticism and acceptance.
The good news? This isn’t a new challenge. Humanity has faced it during every industrial revolution, each time adapting its thinking to a new normal. While I won’t delve into all of these transformative eras — or the ongoing Fourth Industrial Revolution — I’d like to focus on the most recent completed one. Let’s rewind to the Third Industrial Revolution — the dawn of the computer and internet age in the late 20th century — and explore its key ideas of facilitating system adoption.
The 1980s marked a significant turning point in the study of technology adoption, spurred by the rapid rise of personal computers and the challenge of integrating these new tools into everyday life. Researchers quickly recognized the need to focus on factors like user involvement in the design and implementation of information systems. This emphasis acknowledged a simple truth: technology is only as effective as its ability to meet the needs of the people who use it.
On the practical side, industry practitioners concentrated on developing and refining system designs, aiming to make them more user-friendly and effective. My favorite example is research at Xerox PARC (Palo Alto Research Center), where researchers closely observed office workers’ behaviors and workflows. Their insights led to the creation of the desktop metaphor, introducing familiar concepts like files, folders, and a workspace that mirrored physical desks. This innovation revolutionized graphical user interfaces (GUIs), laying the foundation for systems like Apple’s Macintosh and Microsoft Windows. The Dream Machine by M. Mitchell Waldrop or Dealers of Lightning by Michael Hiltzik share more details about history and impact of Xerox PARC.
These parallel efforts — academic research and hands-on development — led to the creation of numerous theories and frameworks to better understand and guide technology adoption. Among these frameworks, the Technology Acceptance Model (TAM) stands out as one of the most influential.
Back in 1986, Fred Davis created it to answer a simple but pivotal question: why do some people adopt new technology while others resist? TAM was designed to measure this adoption process by focusing on customer attitudes — specifically, whether the technology feels useful and easy to use. These two factors form the foundation of the model, offering a lens to understand how people decide to embrace (or avoid) new tools and systems.
The first factor, perceived usefulness — is how much a user believes the technology will improve their performance or productivity. It’s outcome-oriented, zeroing in on whether the tool helps users achieve their goals, complete tasks faster, or deliver better results.
The second factor of TAM is perceived ease of use — the belief that using the technology will be simple and free of unnecessary effort. While usefulness might get a user’s attention, ease of use determines whether they’ll stick with it. If a system feels complicated, clunky, or overly technical, even its benefits might not be enough to win users over. People naturally gravitate toward tools that feel intuitive.