In 1981, Apple’s Steve Jobs described the computer as “a bicycle for the mind,” drawing a provocative parallel between how bicycles enhance human physical capabilities and how computers amplify our mental faculties.
Jobs was particularly struck by a Scientific American article that analysed the efficiency of various species’ locomotion, which showed humans to be relatively inefficient compared to most animals. However, when riding a bicycle, humans became far more efficient than any other species.
Jobs’ bicycle metaphor emphasised efficiency, highlighting how technology could amplify human cognitive capabilities just as bicycles amplify our physical ones. However, traditional bicycles not only provide efficient transportation but simultaneously promote cardiovascular health through physical exertion.
Higher levels of physical fitness can then compound the potential efficiency of the machine.
The emergence of e-bikes has changed all this. While e-bikes excel at transportation efficiency, often requiring minimal physical effort, they fundamentally alter the dual purpose nature of bicycle transportation. The health benefits traditionally associated with cycling diminish significantly or disappear entirely when the motor does most or all
of the work.
The effect of the emergence of e-bikes mirrors the current situation with generative AI in education. Like an e-bike, AI tools can efficiently transport students to their destination – a completed assignment or perhaps their entire degree – but potentially eliminate the valuable mental effort inherent in the learning process.
This raises a fundamental question about education’s purpose: Should we prioritise getting students from point A to point B with minimal effort, or does the true value lie in the mental exertion required for that journey? The cognitive effort – the mental sweat – involved in learning plays a crucial role in developing understanding, critical thinking skills, and long-term knowledge retention.
Our current educational systems prioritise outcomes over processes. This outcomes focused approach naturally leads to a pragmatic question: If the goal is simply reaching the destination, why not use whatever tools allow us to arrive there most efficiently?
Can we blame students for using whatever tools they have to reach the end goal when we have created systems so heavily focused on that end result and not the journey to get there?
We must redirect our focus to the inherent benefits of the learning process itself. This shift in perspective provides a foundation for students to use AI not merely as a shortcut to predetermined end results but as a tool to explore new territories – not just A to B but to points C, D, E, and beyond. The ultimate goal should be humans and machines working together to push the boundaries of knowledge and understanding. The way AI is integrated into education needs to align with this goal.
However, this collaborative future becomes impossible if we allow AI to do all the work.
Just as an e-bike rider who never pedals loses the health benefits of cycling, students who over-rely entirely on AI for their learning miss the crucial mental development that comes from grappling with complex ideas and problems.
The path forward requires carefully balancing AI assistance with necessary mental effort, ensuring that AI serves as a true bicycle for the mind – amplifying our capabilities rather than replacing the vital work that high-quality learning requires.
Acknowledgement: Claude (Sonnet 3.5) was used to edit this article
Professor Jason Lodge is Director of the Learning, Instruction, and Technology Lab in the School of Education at The University of Queensland
